DESCRIPTION
TITLE OF INVENTION
REPRODUCTION SIGNAL EVALUATION METHOD, REPRODUCTION SIGNAL
EVALUATION UNIT, AND OPTICAL DISK DEVICE ADOPTING THE SAME
TECHNICAL FIELD
[0001] The present invention relates to a reproduction signal evaluation method and
reproduction signal evaluation unit using a PRML signal processing system, and an optical disk
device adopting the same.
BACKGROUND ART
[0002] Recently the shortest mark length of recording marks have reached the limit for optical
resolution, and an increase in inter-symbol interference and deterioration of SNR (Signal Noise
Ratio) are becoming obvious as the density of optical disk media increases, therefore the use of a
PRML (Partial Response Maximum Likelihood) system as a signal processing method is
becoming common.
[0003] The PRML system is a technology combining partial response (PR) and maximum
likelihood (ML) decoding, and is a known system for selecting a most likely signal sequence
from a reproduced waveform, assuming the occurrence of inter-symbol interference. As a result,
it is known that decoding performance improves compared with a conventional level decision
system, (e.g. see Non Patent literature 1)
[0004] On the other hand, a shift in signal processing systems from a level decision to PRML
has resulted in generating some problems in reproduction signal evaluation methods. Jitter, that
is a reproduction signal evaluation index, which has been used conventionally, is based on the
assumption that signals are processed using a level decision system. This means that in some
cases jitter has no correlation with the decoding performance of the PRML system, of which
signal processing algorithms are different from the level decision system. Therefore a new index
having correlation with the decoding performance of the PRML system has been proposed (e.g.
see Patent literature 1 and Patent literature 2).
[0005] A new index to position shift (edge shift) between a mark and a space, which is critical
for recording quality of an optical disk, has also been proposed (e.g. see Patent literature 3). If a
PRML system is used, this index must also be correlated with the performance of the PRML, and
must quantitatively express the shift direction and quantity of the edge for each pattern,
according to the concept of the PRML system.
[0006] As the density of magnetic disk media increases further, the problem of inter-symbol

interference and SNR deterioration becomes more serious. In this case, the system margin can
be maintained by using a higher level PRML system (e.g. see Non Patent literature 1). In the
case of an optical disk medium of which diameter is 12 cm and recording capacity per recording
layer is 25 GB. The system margin can be maintained by using a PR1221 ML system, but in the
case of a 33.3 GB recording capacity per recording layer, a PR12221 ML system must be used.
In this way. it is expected that the tendency to use a higher level PRML system would continue
in proportion to the increase in densities of optical disk media.
[0007] Patent literature 1 and Patent literature 2 disclose using "a differential metric, which is
a difference of the reproduction signals between the most likely first state transition sequence
and the second most likely second state transition sequence" as the index value.
[0008] If there are a plurality of patterns of "a most likely first state transition sequence and
second most likely second state transition sequence" which have the possibility of causing an
error, these patterns must be statistically processed systematically. This processing method is
not disclosed in Patent literature 1 and Patent literature 2. Patent literature 5 discloses a method
for detecting a plurality of patterns of "a differential metric of reproduction signals between the
most likely first state transition sequence and the second most likely second state transition
sequence" detected in the same manner as in Patent literature 1 and Patent literature 2, and the
processing of a pattern group.
[0009] In PR12221 ML signal processing, which is disclosed in Patent literature 5, there are
three types of patterns which easily cause an error (pattern group of merging paths of which
Euclidian distance is relatively short). In this pattern group. The pattern generation probability
and the number of errors, when the pattern generates errors occur in a pattern, differ depending
on the pattern, so according to Patent literature 5, a standard deviation a is determined from the
distribution of the index values, which are acquired for each pattern, and the errors to be
generated are predicted based on the generation probability of the pattern (generation frequency
with respect to all parameters) and the number of errors to be generated when the pattern has an
error.
[0010] In Patent literature 5, a method for assuming the distribution of the acquired index
values as a normal distribution and predicting a probability for the index value becoming "0" or
less based on the standard deviation a thereof and variance average value µ, that is, a probability
of generation of a bit error, is used as an error prediction method. This, however, is a general
method for predicting error generation probability. The method for calculating the predicted
error rate according to Patent literature 5 is characterized in that generation probability is
determined for each pattern, the predicted error rate is calculated, and this predicted error rate is
used as a guideline of signal quality.

[0011] However, with the method according to Patent literature 5. the error rate cannot be
predicted accurately if recording distortion occurs to recording signals. This problem becomes
particularly conspicuous when data is recorded by thermal recording, such as the case of an
optical disk, since recording distortion tends to be generated by thermal interference. As the
density of optical disk increases, space between recording pits decreases even more, and an
increase in thermal interference is expected, therefore this problem will be unavoidable in the
future. The problem of the predicted error rate calculation method according to Patent literature
5, which cannot appropriately evaluate the signal quality of signals having recording distortion,
will now be described.
[0012] Fig. 21 shows an example of frequency distribution of a differential metric of a
specific pattern, which is used as a signal index in Patent literature 1 and Patent literature 5.
Generally speaking, the spread of the distribution of the differential metric is caused by the noise
generated in an optical disk. The reproduction noise generated in an optical disk is random, so
this distribution usually is a normal distribution. And this differential metric is defined as a
"differential metric of the most likely first state transition sequence and second most likely
second state transition sequence", and is a distribution of which center is a square of the
Euclidean distance between the most likely first state transition sequence and the second most
likely second state transition sequence of an ideal signal (hereafter defined as the signal
processing threshold). The standard deviation of which center is this signal processing threshold
is the index value defined in Patent literature 1, Patent literature 2 and Patent literature 5. The
probability of this differential metric becoming 0 or less corresponds to the predicted error rate.
This predicted error rate can be determined using the inverse function of the cumulative
distribution function of this normal distribution.
[0013] Fig. 21A is a distribution diagram when no substantial distortion occurred during
recording, and Fig. 21B and Fig. 21C show distribution diagrams in a state where recording
edges in the recording pits shifted due to thermal interference during recording, and recording
distortion occurred. If distortion occurs due to thermal interference, the frequency distribution of
the differential metric of a specific pattern becomes a normal distribution of which center value
is shifted. This shift of the center position corresponds to the distortion generated by thermal
interference. Fig. 21B and Fig. 21C are cases when a predetermined amount of shift occurred in
the plus or minus direction from the center of the distribution, and an index value to be
determined is the same value for both Fig. 21B and Fig. 21C, and the index value increases since
the center of the distribution has shifted. An increase in the index value should mean an increase
in the probability of error generation, but errors decrease in the case of Fig. 21C.
[0014] This is because in the case of Fig. 21 B: where the center of the distribution is shifted to

the side closer to "0", error generation probability (probability of differential metric becoming 0
or less) increases, but in the case of Fig. 21C, where the center of the distribution is shifted to the
plus side, error generation probability decreases. This reversal phenomena is because an error is
generated only when the index value based on the differential metric approaches 0, which is the
major difference from the jitter of the time axis, that is the index value conventionally used for
optical disks. In the case of a conventional jitter of the time axis, errors increase regardless the
side, plus or minus, to which the center position of the distribution shifts, therefore the above
mentioned problem does not occur.
[0015] A problem similar to the above also occurs in the case shown in Fig. 21D. Fig. 21D is
a case when the determined distribution of the differential metric is not normal distribution. This
occurs when the thermal interference during recording is high, and thermal interference is also
received from the recording marks before and after "the most likely first state transition sequence
and second most likely second state transition sequence". The thermal interference amount is
different depending on the length of the recording marks before and after, and the shift of
recording mark positions generates a differential metric distribution where two normal
distributions (distribution 1 and distribution 2) overlap.
[0016] In distribution 2, where there is a shift to the plus side from the signal processing
threshold, error generation probability drops, but the index value, which is a standard deviation
from the signal processing threshold as the center, increases because of the influence of
distribution 2. Just like the case of Fig. 21C, error rate also decreases when the index value
increases. In this way, if the prior art reported in Patent literature 1 and Patent literature 5 is
applied to a high recording density optical disk of which thermal interference is high, the
correlation of the index value and error rate worsens.
[0017] An idea for solving this problem is disclosed in Patent literature 4. This is a method of
counting a number of differential metrics with which the differential metric, acquired from a
predetermined pattern group, which becomes smaller than a predetermined threshold (e.g. half of
signal processing threshold). A method for determining a predicted error rate based on this
count value is also disclosed. In the case of this method, a side closer to 0 of the differential
metric distribution, that is the side which has a possibility of generating an error, is used for the
evaluation target, so the above mentioned problems in Patent literature 1 and Patent literature 5
do not occur. But a new problem, mentioned herein below, occurs, since a predetermined
threshold is used and a number of differential metrics exceeding this threshold is measured. This
problem will be described with reference to Fig. 21E.
[0018] Fig. 21E shows an example of counting the differential metrics of the distribution
which exceeds the threshold, which is half of the signal processing threshold. The differential

metrics less than this threshold are counted, and the ratio of the parameter of pattern generation
and the count value is used as the signal index. If it is assumed that the distribution of the
differential metric is a normal distribution based on this count value, the probability when the
differential metric becomes smaller than 0 can be determined, and the predicted error rate can be
calculated. Fig. 2 IF shows an example of frequency distribution when the signal quality is good
(signal quality with about an 8% jitter). In such a case, the spread of the distribution of the
differential metric becomes narrow, and the number of differential metrics which exceeds the
threshold decreases dramatically.
[0019] In the case of Fig. 21F, only about 0.2%. out of the differential metric distribution, can
be measured. This means that a wide area must be measured in order to increase the accuracy of
the measurement, which increases the measurement time and diminishes measurement stability.
Also if there are defects and scratches generated during manufacture of the disks or if there is
dust on a disk surface, the differential metric is generated in an area not greater than the
threshold due to this defect (illustrated in Fig. 21F). In such a case, a number of the differential
metrics, which exceed the threshold generated in the normal distribution, cannot be counted
correctly. An advantage of conventional time axis jitter used for optical disks is that it is not
affected by such defects, since standard deviation of measured time fluctuation is used and all
the measured data is used.
[0020] The method disclosed in Patent literature 4, on the other hand, does not have this
advantage of the conventional method based on time axis jitter, which is not affected by the
defects, and therefore has a problem when used for the index values of optical disks where such
defects as scratches and fingerprints easily occur. In order to increase the number of differential
metrics to be measured using the method according to Patent literature 4, the threshold could be
increased, but if the threshold is increased, another problem occurs, that is the accuracy of the
predicted error rate drops. In an extreme case, if the threshold is increased to half of the
Euclidean distance, a number of differential metrics that exceed the threshold becomes half of
the number of measured samples, therefore it no longer depends on the spread of distribution,
and accurate measurement becomes possible. In this way, in the case of the method according to
Patent literature 4, the value of the threshold must be adjusted in order to maintain constant
measurement accuracy depending on the quality of measured signals, and such adjustment is
possible if the manner of how distribution spreads is somewhat understood, nonetheless this is a
major problem for optical disks, where signal quality changes significantly.
[0021] Patent literature 4 and No. Patent literature 5 also disclose a method of using bER
predicted by the differential metric as the index, but if this is used as an index value,
compatibility with the time axis jitter, which has been used as the signal quality evaluation index

of optical disks, is lost, and handling is difficult.
CITATION LIST
PATENT LITERATURE
[00221 Patent literature i: JP 2003-141S23A
Patent literature 2: JP 2004-213S62A
Patent literature 3: JP 2004-335079A
Patent literature 4: JP 2003-51163A
Patent literature 5: JP 2003-272304A
NON PATENT LITERATURE
[0023] Non Patent literature 1: Blue-ray Disk Books, supervised by Hiroshi Ogawa and
Shinichi Tanaka, Ohmsha Ltd, Dec. 10, 2006
Non Patent literature 2: Yoji Iikuni, "Adaptive Signal Processing Algorithms",
Baihukan, July 2000
SUMMARY OF INVENTION
[0024] The present invention aims to solve the above problem and provides a signal
processing method and reproduction signal evaluation unit that allow the quality of reproduction
signals from information recording medium to be evaluated with high accuracy, and an optical
disk device adopting the same.
[0025] A reproduction signal evaluation method according to an aspect of the present
invention is a reproduction signal evaluation method for evaluating a quality of a reproduction
signal reproduced from an information recording medium based on a binary signal generated
from the reproduction signal using a PRML signal processing system, the method having: a
pattern extraction step of extracting, from the binary signal, a specific state transition pattern
which has the possibility of causing a bit error; a differential metric computing step of computing
a differential metric, which is a difference of a first metric between an ideal signal of a most
likely first state transition sequence corresponding to the binary signal and the reproduction
signal, and a second metric between an ideal signal of a second most likely second state
transition sequence corresponding to the binary signal and the reproduction signal, based on the
binary signal of the state transition pattern extracted in the pattern extraction step: a first
integration step of integrating the differential metric computed in the differential metric
computing step; a first count step of counting a number of times of integration processing in the
first integration step: a differential metric extraction step of extracting the differential metric not
greater than a predetermined signal processing threshold; a second integration step of integrating
the differential metric not greater than the signal processing threshold, extracted in the

differential metric extraction step; a second count step of counting a number of times of
integration processing in the second integration step; an error rate computing step of computing
an error rate that is predicted based on an integration value that is integrated in the first
integration step, a count value that is counted in the first count step, an integration value that is
integrated in the second integration step, and a count value that is counted in the second count
step; a standard deviation computing step of computing a standard deviation based on the error
rate that is computed in the error rate computing step; and an evaluation step of evaluating a
quality of the reproduction signal using the standard deviation computed in the standard
deviation computing step.
[0026] According to the present invention, when the mean value of the differential metrics
does not match with the code distance of the ideal signal, an error of the standard deviation,
which is generated by the shift of the mean value of the differential metrics from the code
distance of the ideal signal, is corrected using the computed integration value of the differential
metrics and the count value of a number of times of integration processing of the differential
metrics, whereby correlation of the error rate and the signal index value is improved, and the
quality of the reproduction signal reproduced from an information recording medium can be
evaluated at high accuracy.
[0027] Other objects, characteristics and advantages of the present invention shall be
sufficiently clarified by the description herein below.
BRIEF DESCRIPTION OF DRAWINGS
[0028] Fig. 1 is a block diagram depicting a general structure of an optical disk device
according to one embodiment of the present invention;
Fig. 2 is a block diagram depicting a general structure of an optical disk device
according to another embodiment of the present invention;
Fig. 3 is a diagram depicting a state transition rule which is determined by an RLL (1,
7) recording code and equalization type PR (1, 2, 2, 2, 1) according to one embodiment of the
present invention;
Fig. 4 is a trellis diagram corresponding to the state transition rule shown in Fig. 3;
Fig. 5 is a graph depicting a relationship of a sampling time and a reproduction level
(signal level) on the transition paths in Table 1;
Fig. 6 is a graph depicting a relationship of a sampling time and a reproduction level
(signal level) on the transition paths in Table 2;
Fig. 7 is a graph depicting a relationship of a sampling time and a reproduction level
(signal level) on the transition paths in Table 3:

Fig. S is a diagram depicting a distribution of the differential metric of the PR (1. 2. 2.
2. 1) ML according to one embodiment of the present invention;
Fig. 9 is a diagram depicting a distribution of the differential metric in a Euclidian
distance pattern of a PR (1. 2. 2. 2. 1) ML according to one embodiment of the present invention;
Fig. 10 is a diagram depicting a distribution of the differential metric in each Euclidian
distance pattern of a PR (1, 2, 2. 2, 1) ML according to one embodiment of the present invention;
Fig. 11 is a diagram depicting a distribution of the differential metric of a PR (1, 2. 2. 2.
1) ML according to one embodiment of the present invention;
Fig. 12 is a graph depicting a relationship of the signal evaluation index value and
error rate according to one embodiment of the present invention;
Fig. 13 is a block diagram depicting a structure of an optical disk device according to
still another embodiment of the present invention;
Fig. 14 is a block diagram depicting a structure of an optical disk device according to
still another embodiment of the present invention;
Fig. 15 is a block diagram depicting a structure of an optical disk device according to
still another embodiment of the present invention;
Fig. 16A is a diagram depicting a distribution of the differential metrics according to
the third and fourth embodiments;
Fig. 16B is a diagram depicting a distribution of the differential metrics according to
the fifth embodiment;
Fig. 17A and Fig. 17B are diagrams depicting a standard deviation computing method
according to the fifth embodiment;
Fig. 18 is a graph depicting a relationship of a variable a1(ax) and standard deviation
σ1/2(σx/2) when the mean value of the differential metrics is a variable b1(bx):
Fig. 19 is a block diagram depicting a structure of an optical disk device according to
still another embodiment of the present invention;
Fig. 20A and Fig. 20B are diagrams depicting a standard deviation computing method
according to the sixth embodiment;
Fig. 21A is a diagram depicting the distribution of a conventional differential metric;
Fig. 21B is a diagram depicting the distribution of a conventional differential metric;
Fig. 21C is a diagram depicting the distribution of a conventional differential metric;
Fig. 21D is a diagram depicting the distribution of a conventional differential metric;
Fig. 21E is a diagram depicting the distribution of a conventional differential metric;
and
Fig. 21F is a diagram depicting the distribution of a conventional differential metric.

EMBODIMENTS FOR CARRYING OUT THE INVENTION
[0029] Embodiments of the present invention will now be described with reference to the
drawings. The following embodiments are examples of carrying out the present invention, and
do not limit the technical scope of the present invention.
[0030] In a signal evaluation index detection unit of the present embodiment, a PR 12221 ML
system, which is an example of a PRML system, is used for signal processing of a reproduction
system, and RLL (Run Length Limited) codes, such as an RLL (1. 7) code, are used for the
recording codes. A PRML system is a signal processing that combines waveform equalization
technology for correcting reproduction distortion, which is generated when information is
reproduced, and signal processing technology for selecting a most likely data sequence from the
reproduction signal which includes data errors, by actively utilizing the redundancy of an
equalized waveform.
[0031] First signal processing by a PR12221 ML system will be' described in brief, with
reference to Fig. 3 and Fig. 4.
[0032] Fig. 3 is a state transition diagram depicting the state transition rule, which is
determined by the RLL (1, 7) recording codes and the PR12221 ML system. Fig. 3 shows a state
transition diagram which is normally used when PR ML is described. Fig. 4 is a trellis diagram
in which the state transition diagram shown in Fig. 3 is developed with respect to the time axis.
[0033] "0"' or "1'' inside the parenthesis in Fig. 3 indicates a signal sequence on the time axis,
and indicates the state of possibility of the state transition from the respective state to the next
time.
[0034] In a PR12221 ML system, a number of states of the decoding unit is limited to 10,
because of the combination with the RLL (1, 7) code. A number of state transition paths in a
PR12221 ML system is 16, and a number of reproduction levels is 9.
[0035] In order to describe the state transition rule of a PR12221 ML system, 10 states are
represented, as shown in the state transition diagram in Fig. 3, where the state S(0, 0, 0, 0) at a
certain time is SO. the state S(0, 0, 0, 1) is S1, the state S(0, 0, 1, 1,) is S2, the state S(0,1,1, 1) is
S3, the state S(1, 1, 1, 1) is S4, the state S(1, 1, 1, 0) is S5, the state S(1, 1, 0, 0) is S6, the state
S(1, 0, 0, 0) is S7, the state S(1, 0, 0, 1) is SS and the state S(0, 1, 1, 0) is S9. In Fig. 3, "0" or
"1" in parenthesis indicates a signal sequence on the time axis, and shows which state a certain
state may possibly become in the state transition the next time.
[0036] In the state transition of the PR12221 ML system shown in Fig. 4, there are an infinite
number of state transition sequence patterns (combination of states) in which two state
transitions can occur when a predetermined state at a certain time transit to a predetermined state

at another time. However, patterns which have a high possibility of causing an error are limited
to specific patterns of which discernment is difficult. By targeting these state transition patterns
in particular which can easily generate an error, the state transition sequence patterns in the
PR12221 ML system can be listed as shown in Table 1, Table 2 and Table 3.
[0037] [Table 1]

[0040] In each of the tables. Table 1 to Table 3. shows a state transition to indicate a locus of
states which merged from the start state, two possible transition data sequences which underwent
state transition, two possible ideal reproduction waveforms which underwent state transition, and
a square of the Euclidean distance of the two ideal reproduction waveforms.
[0041] The square of the Euclidean distance indicates as sum of the square of the difference of
the two ideal reproduction waveforms. When the error possibility of the two reproduction
waveforms is judged, two reproduction waveforms can be more easily distinguished if the value
of the Euclidean distance is long, therefore a judgment mistake occurs less frequently. If the
value of the Euclidean distance is short, a judgment mistake may more frequently occur, since it
is difficult to distinguish the two waveforms having an error possibility. In other words, state
transition patterns of which Euclidean distance is long are state transition patterns where an error
does not occur very much, and state transition patterns when the Euclidean distance is short are
state transition patterns where an error easily occurs.
[0042] In each table, the first column shows the state transition (Smk-9 → Snk) where two state
transitions, which easily cause an error, branch and merge again. The second column show's the
transition data sequence (bk-i, .. ., bk) which generates this state transition. X in this state data
sequence indicates a bit which has high error generation possibility among this data, and if this
state transition is judged as erred, a number of X (also !X in Table 2 and Table 3) is a number of
errors. In other words, X in a transition data sequence can be either "0" or "1". One of "0" or
"1" corresponds to the most likely first state transition sequence, and the other corresponds to the
second most likely second state transition sequence. In Table 2 and Table 3, !X indicates a bit
inversion of X.
[0043] As described in detail later, each decoding data sequence (binary signal) after a Viterbi
decoding section executes decoding processing is compared with the transition data sequences in
Table 1 to Table 3 (X indicates "don't care''), and a most likely first state transition sequence
having high error possibility and a second most likely second state transition sequence are
extracted. The third column shows the first state transition sequence and second state transition
sequence. The fourth column shows two ideal reproduction waveforms (PR equalization ideal
values) when a respective state transitions completes, and the fifth column shows a square of the
Euclidean distance of these two ideal signals (square of Euclidean distance between paths).
[0044] Table 1 shows state transition patterns that could take two state transitions, and is state
transition patterns in the case when a square of the Euclidean distance is "14". There are 18
types of state transition sequence patterns in the case when a square of the Euclidean distance is
14. The state transition sequence patterns shown in Table 1 correspond to the edge section
(switching of a mark and a space) of the waveforms of an optical disk. In other words, the state

transition sequence pattern shown in Table 1 is a pattern of a 1-bit shift error at the edge.
[0045] Fig. 5 is a graph depicting the relationship of the sampling time and a reproduction
level (signal level) in the transition paths in Table 1. In the graph in Fig. 5, the x-axis indicates a
sampling time (each sampling timing of a recording sequence), and the y-axis indicates a
reproduction level. As mentioned above, in the case of a PR12221 ML system, there are 9 levels
of ideal reproduction signal levels (1evels 0 to S).
[0046] As an example, the transition paths when transiting from the state SO (k-5) to the state
S6 (k) according to the state transition rule shown in Fig. 3 will be described (see Table 1). In
this case, one transition path is a case when the recording sequence was detected as a transition
of "0, 0, 0, 0, 1,1, 1, 0, 0'*. If this transition is converted into a recording state, regarding "0"' of
the reproduction data as a space portion and "1" as a mark portion, the recording state is 4T or
longer spaces, and 3T marks and 2T or longer spaces. In Fig. 5, the relationship of the sampling
time and the reproduction level (signal level) in this transition path is shown as a path A
waveform.
[0047] The other transition path of the state transition paths from the state SO (k-5) to the state
S6 (k) in the state transition rule in Fig. 5 is a case when the recording sequence is detected as
the transition of "0, 0, 0, 0, 0, I, I, 0, 0". If "0" of the reproduction data is regarded as a space
portion and "1" as a mark portion, the recording state corresponds to 5T or longer spaces, and 2T
marks and 2T or longer spaces. In Fig. 5, the PR equivalent ideal waveform of this path is
shown as a path B waveform. The state transition pattern of which square of the Euclidean
distance is 14 in Table 1 always includes one edge information (zero cross point), which is
characteristic thereof.
[004S] Fig. 6 is a graph depicting the relationship of the sampling time and the reproduction
level (signal level) in the transition paths in Table 2. In the graph in Fig. 6, the x-axis indicates a
sampling time (each sampling time of recording sequence), and the y-axis indicates a
reproduction level.
[0049] Table 2 shows state transition patterns which could take two state transitions, just like
Table 1, and shows the state transition patterns in the case when a square of the Euclidean
distance is 12. There are 18 types of state transition patterns in the case when a square of the
Euclidean distance is 12. The state transition patterns shown in Table 2 are patterns having a 2T
marks or 2T spaces shift error, that is 2-bit shift error patterns.
[0050] In this case, one path of which recording sequence transits as '"0, 0, 0, 0. 1, .1, 0, 0, 0, 0,
0" is detected, and if "0" of the reproduction data is regarded as a space portion and "1" as a
mark portion, the recording state corresponds to 4T or longer spaces, and 2T marks and 5T or
longer spaces. In Fig. 6, the PR equivalent ideal waveform of this path is shown as a path A

waveform.
[0051] As an example, the transition paths when transiting from the state SO (k-7) to the state
SO (k) according to the state transition rule shown in Fig. 3 will be described (see Table 2). In
this case, one transition path is a case when the recording sequence was detected as a transition
of "0, 0, 0, 0. 1, 1, 0. 0, 0. 0, 0". If this transition path is converted into a recording state,
regarding "0" as the reproduction data as a space portion and "1"' as a mark portion, the
recording state corresponds to 4T or longer spaces, and 2T marks and 5T or longer spaces. In
Fig. 6, the relationship of the sampling time and the reproduction level (signal level) in this
transition path is shown as a path A waveform.
[0052] The other transition path, on the other hand, is a case when the recording sequence is
detected as the transition of "0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0". If this transition path is converted into
a recording state, regarding "0" of the reproduction data as a space portion and "1" as a mark
portion, the recording state corresponds to 5T or longer spaces, and 2T marks and 4T or longer
spaces. In Fig. 6, the relationship of the sampling time and the reproduction level (signal level)
in this transition path is shown as a path B waveform. The state transition pattern of which
square of the Euclidean distance is 12 in Table 2 always includes two edge information, the rise
and fall of 2T, which is characteristic thereof.
[0053] Fig. 7 is a graph depicting the relationship of the sampling time and the reproduction
level (signal level) in the transition paths in Table 3. In the graph in Fig. 7, the x-axis indicates a
sampling time (each sampling timing of recording sequence), and the y-axis indicates a
reproduction level.
[0054] Table 3 shows state transition sequence patterns which could take two state transition
sequences, just like Table 1 and Table 2, and shows the state transition sequence patterns in the
case when a square of the Euclidean distance is 12. There are 18 types of state transition
sequence patterns in the case when a square of Euclidean distance is 12. The state transition
sequence patterns show:n in Table 3 are areas where 2T marks and 2T spaces continue, that is 3-
bit shift error patterns.
[0055] As an example, the transition paths when transiting from the state SO (k-9) to the state
S6 (k) according to the state transition rule shown in Fig. 3 will be described (see Table 3). In
this case, one transition path is a case when the recording sequence was detected as a transition
of "0, 0, 0, 0, 1, 1, 0. 0, 1, 1, 1, 0, 0". If this transition path is converted into a recording state,
regarding "0" of the reproduction data as a space portion and "1" as a mark portion, the
recording state corresponds to 4T or longer spaces, and 2T marks, 2T spaces, 3T marks and 2T
or longer spaces. In Fig. 7, the relationship of the sampling time and the reproduction level
(signal level) in this transition path is shown as a path A waveform.

[0056] The other transition path, on the other hand, is a case when the recording sequence is
detected as the transition of "0, 0, 0. 0, 0. 1, 1. 0. 0, 1, 1, 0. 0". If this transition path is converted
into a recording state, regarding "0" of the reproduction data as a space portion and "1" as a
mark portion, the recording state corresponds to 5T or longer spaces, and 2T marks, 2T spaces,
2T marks and 2T or longer spaces. In Fig. 7. the relationship of the sampling time and the
reproduction level (signal level) in this transition path is shown as a path B waveform. The state
transition sequence pattern of which square of the Euclidean distance is 12 in Table 3 always
includes at least three edge information, which is characteristic thereof.
[0057] Embodiments of the present invention will now be described.
[0058] (First Embodiment)
An optical disk device having a reproduction signal evaluation unit according to one
embodiment of the present invention will now be described with reference to the drawings. Fig.
1 is a block diagram dcpicting a structure of the optical disk device 200 according to the first
embodiment.
[0059] An information recording medium 1 is an information recording medium for optically
recording/reproducing information, and is an optical disk medium, for example. The optical disk
device 200 is a reproduction unit which reproduces information from the installed information
recording medium 1.
[0060] The optical disk device 200 has an optical head section 2, a preamplifier section 3, an
AGC (Automatic Gain Controller) section 4, a waveform equalization section 5, an AT)
conversion section 6, a PLL (Phase Locked Loop) section 7, a PR equalization section 8, a
maximum likelihood decoding section 9, a signal evaluation index detection section
(reproduction signal evaluation unit) 100, and an optical disk controller section 15.
[0061] The optical head section 2 converges laser beams transmitted through an objective lens
onto a recording laver of the information recording; medium 1, receives the reflected light thereof,
and generates analog reproduction signals which indicate information read from the information
recording medium 1. The preamplifier section 3 amplifies an analog reproduction signal, which
is generated by the optical head section 2, using a predetermined gain, and outputs it to the AGC
section 4. A numerical aperture of the objective lens is 0.7 to 0.9, and is more preferably 0.85.
The wavelength of the laser beam is 410 nm or less, and is more preferably 405 nm.
[0062] The preamplifier unit 3 amplifies the analog reproduction signal by a predetermined
gain, and outputs it to the AGC section 4.
[0063] The AGC section 4 amplifies or attenuates the analog reproduction signal, and outputs
it to the waveform equalization section 5 based on the output from the AT) conversion section 6.
so that the analog reproduction signal from the preamplifier section 3 has a predetermined

amplitude.
[0064] The waveform equalization section 5 has LPF characteristics to shield a high
frequency area of the reproduction signal, and HPF characteristics to shield a low frequency area
of the reproduction signal, and shapes the reproduction waveform according to desired
characteristics, and outputs it to the AD conversion section 6.
[0065] The AD conversion section 6 samples an analog reproduction signal synchronizing
with a reproduction clock, which is output from the PLL section 7, converts the analog
reproduction signal into a digital reproduction signal, and outputs it to the PR equalization
section 8, and also to the AGC section 4 and the PLL section 7.
[0066] The PLL section 7 generates a reproduction clock to synchronize with the reproduction
signal after waveform equalization, based on the output from the Ay'D conversion section 6, and
outputs it to the A/D conversion section 6.
[0067] The PR equalization section 8 has a function to change the filter characteristics into
characteristics of various PR systems. The PR equalization section 8 performs filtering to be the
frequency characteristics, which is set so that the frequency characteristics of the reproduction
system become assumed characteristics of the maximum likelihood decoding section 9 (e.g. PR
(1, 2, 2, 2,1) equalization characteristics), performs PR equalization processing on digital
reproduction signals for suppressing high frequency noises, intentionally adding inter-symbol
interference, and outputs the results to the maximum likelihood decoding section 9. The PR
equalization section 8 may have an FIR (Finite Impulse Response) filter structure, for example,
so as to adaptively control the tap coefficient using LMS (The Least-Mean Square) algorithm
(see Non Patent literature 2).
[0068] The maximum likelihood decoding unit 9 is a Viterbi decoder, for example, and uses a
maximum likelihood decoding system, which estimates a most likely sequence based on a code
rule intentionally attached according to the type of partial response. This maximum likelihood
decoding section 9 decodes a reproducing signal which was PR-equalized by the PR equalization
section 8. and outputs binary data. This binary data is output to the optical disk controller
section 15 in a subsequent step, as a decoded binary signal, and after execution of predetermined
processing, information recorded on the information recording medium 1 is reproduced.
[0069] Now a structure of the signal evaluation index detection section 100 according to the
present embodiment will be described. The signal evaluation index detection section 100 has a
pattern detection section 101, a differential metric computing section 102, a level decision
section 103, a pattern count section 104, an integration section 105, an error computing section
116 and a standard deviation computing section 120.
[0070] A waveform-shaped digital reproduction signal which is output from the PR

equalization section S. and a binary signal which is output from the maximum likelihood
decoding section 9. are input to the signal evaluation index detection section 100. In the signal
evaluation index detection section 100. the binary signal is input to the pattern detection section
101, and the digital reproduction signal is input to the differential metric computing section 102,
and evaluation processing is executed on digital reproduction signals of the information
recording medium 1.
[0071] The pattern detection section 101 has a function to extract specific state transition
patterns, which have the possibility to cause a bit error, from a binary signal. The pattern
detection section 101, according to the present embodiment, extracts specific state transition
patterns (state transition patterns shown in Table 1) of which square of the Euclidean distance
between an ideal signal of a most likely first state transition sequence and an ideal signal of a
second most likely second state transition sequence is 14. In order to implement this, the pattern
detection section 101 stores information of the state transition patterns shown in Table 1. And
the pattern detection section 101 compares the transition data sequences in Table 1, and the
binary signal which is output from the maximum likelihood decoding section 9. As a result of
this comparison, if the binary signal matches the transition data sequences in Table 1, this binary
signal becomes an extraction target, and the most likely first state transition sequence and the
most likely second state transition sequence, corresponding to this binary signal, are selected
based on the information in Table 1.
[0072] Based on a binary signal extracted by the pattern detection section 101, the differential
metric computing section 102 computes a "differential metric" which is an absolute value of a
difference of "a first metric between an ideal signal of a most likely first state transition sequence
corresponding to the binary signal (PR equalization ideal value: see Table 1) and a digital
reproduction signal'' and "a second metric between an ideal signal of a second most likely
second state transition sequence corresponding to the binary signal and a digital reproduction
signal'". Here the first metric is a square of the Euclidean distance between the ideal signal of the
first state transition sequence and the digital reproduction signal, and the second metric is a
square of the Euclidean distance between the ideal signal of the second state transition sequence
and the digital reproduction signal.
[0073] The output of the differential metric computing section 102 is input to the level
decision section 103, and is compared with a predetermined value (signal processing threshold).
The pattern count section 104 counts a number of differential metrics which are less than the
signal processing threshold. Each count value is reflected in a pattern group generation
frequency when an error rate is calculated. The integration section 105 integrates the differential
metrics which are less than the signal processing threshold. A mean value of the differentia]

metrics which are not greater than the signal processing threshold can be determined by dividing
the integration value determined by the integration section 105 by the pattern generation count.
The error computing section 116 calculates a predicted error rate based on each integration value
of differential metrics which are not greater than the signal processing threshold, and the pattern
generation count. The standard deviation computing section 120 computes the standard
deviation corresponding to this error rate, and determines this standard deviation as the signal
index value to evaluate the signal quality. The process by this signal evaluation index detection
section 100 will now be described in detail.
[0074] The reproduction signal reproduced from the information recording medium 1 in the
PRML processing is output from the maximum likelihood decoding section 9 as a binary signal,
as mentioned above, and is input to the signal evaluation index detection section 100. When one
of the transition data sequence patterns in Table 1 is detected from this binary signal, the PR
equalization ideal values of the first state transition sequence and the second state transition
sequence are determined. For example, if (0, 0, 0, 0, X, 1, 1, 0, 0) is decoded as the binary signal
in Table 1, (S0, S1, S2, S3, S5, S6) is selected as the most likely first state transition sequence,
and (S0, S0, S1, S2, S9, S6) is selected as the second most likely second state transition sequence.
The PR equalization ideal value corresponding to the first state transition sequence is (1, 3, 5, 6,
5). And the PR equalization ideal value corresponding to the second state transition sequence is
(0,1,3,4,4).
[0075] Then the differential metric computing unit 102 determines a first metric (Pb14) which
is a square of the Euclidean distance between the reproduction signal sequence (digital
reproduction signal) and the PR equalization ideal value corresponding to the first state transition
sequence. In the same way, the differential metric computing unit 102 determines a second
metric (Pa14) which is a square of the Euclidean distance between the reproduction signal
sequence and the PR equalization ideal value corresponding to the second state transition
sequence. The differential metric computing unit 102 determines an absolute value of the
difference between the first metric (Pb14) and the second metric (Pa14), as the differential metric
D14 = | Pa14- Pb14 |. The computing of Pb14 is shown in Expression (1), and the computing of
Pa14 is shown in Expression (2). In these expressions, bk denotes the PR equalization ideal value
corresponding to the first state transition sequence, ak denotes a PR equalization ideal value
corresponding to the second state transition sequence, and xk denotes a reproduction signal
[0076]

[0079] In Fig. 9, an area above the signal processing threshold is an area where error does not
occur, and it is therefore unnecessary to predict an error rate. Hence in order to predict an error
rate based on the standard deviation of the differential metric, only an area not greater than the
signal processing threshold becomes a target. A method for calculating this error rate will now
be described.
[0080] The differential metric D14, which is an output from the differential metric computing
section 102, is input to the level decision section 103, and is compared with a predetermined
value (signal processing threshold). In the present embodiment, the signal processing threshold
according to an extraction target specific state transition pattern is set to "14", which is a square
of the Euclidean distance between an ideal signal of the most likely first state transition sequence
and an ideal signal of the second most likely second state transition sequence. If the differential
metric D14 is not greater than the signal processing threshold "14", the level decision section 103
outputs the value of this differential metric D14 to the integration section 105, and the pattern
count section 104 counts up the count value. The integration section 105 integrates the
differential metric cumulatively each time the differential metric D14, which is not greater than
the signal processing threshold, is input. Then the error computing section 116 calculates a
predetermined error date using an integration value of the differential metric not greater than the
signal processing threshold and number of generated patterns, counted by the pattern count
section 104. Operation of the error computing section 116 will now be described.
[0081] The mean value of the differential metrics which are not greater than the signal
processing threshold can be determined by dividing the integration value, determined by the
integration section 105. by a number of differential metrics less than the signal processing
threshold (number of generated patterns), which was counted up by the pattern count section 104.
When it is assumed that the mean value of the differential metrics, which are not greater than the
signal processing threshold, is M(x). the mean value of the distribution functions is µ, the

standard deviation is Σ14, the probability density function is f. and the distribution function is a
normal distribution, and the absolute mean value m of the differential metrics, which are less
than the signal processing threshold, is given by the following Expression (4).
[0082]

[0083] Therefore the relationship of the standard deviation Σ14 of the differential metrics,
which are not greater than the signal processing threshold and the absolute mean value m of the
differential metrics, which are not greater than the signal processing threshold, is determined by
the following Expression (5).
[0084]

[0085] As Expression (4) and Expression (5) show, in order to determine the standard
deviation σ14 of the differential metrics which are not greater than the signal processing threshold.

the absolute mean value m of the differential metrics, which are not greater than the signal
processing threshold, is determined, and is then multiplied by about 1.253. Since the signal
processing threshold is fixed, the standard deviation σ14 can be calculated from the absolute
mean value m. The probability of error generation (error rate bER14). which is computed by the
error computing section 116, can be determined by the following Expression (6).
[0086]

[0087] Here d14 in Expression (6) denotes the Euclidean distance between an ideal signal of
the most likely first state transition sequence in the extraction target state transition patterns, and
an ideal signal of the second most likely second state transition sequence. In the case of the
present embodiment, a square of the Euclidean distance d142 = 14 is used. Therefore if the
standard deviation given by Expression (5), which is determined by the integration value and
integration count, is Σ14, then the error rate bER14B, predicted based on the computing of the
error computing section 116, is given by the following Expression (7). p14 (= 0.4) is an error
generation probability in the distribution components with respect to all the channel points.
Errors generated in the state transition sequence patterns in Table 1 are 1-bit errors, so the error
generation probability has been multiplied by 1.
[0088] The standard deviation computing section 120 converts this error rate (error generation
probability) bER14 into a signal index value M, to make it to an index which can be handled in a
similar manner as a jitter. By using Expression (7), the standard deviation computing unit 120
converts bER14 into signal index value M using the standard deviation a corresponding to the
predicted error rate.
[0089]

[0090] Here erfc() is an integration value of the complementary error function. If the defining
expression of the signal index value M according to the present embodiment is the following
Expression (8). then the index value M using a virtual standard deviation a can be determined by
substituting bER14, calculated by Expression (6), in Expression (7).
[0091]


[0092] In the above description, a virtual standard deviation a and signal index value M are
calculated from a predicted error rate using Expression (6) to Expression (S).
[0093] As described above, according to the present embodiment, the state transition sequence
patterns of merging paths of which Euclidean distance in the PRML signal processing is
relatively small are targeted, and the signal evaluation index M is generated based on the
differential metric information of the state transition sequence patterns. Specifically, a predicted
error rate is calculated from the mean value of the differential metric information which is not
greater than the signal processing threshold, then the virtual standard deviation a is calculated
from the error rate, and the signal evaluation index M including this standard deviation a of the
normal distribution is generated. As a result, it is possible to realize a signal evaluation method
and evaluation index having very high correlation with the error rate.
[0094] In the case of a conventionally proposed distribution evaluation of a simple differential
metric, it is difficult to calculate a signal index having correlation with an error rate, because of
the recording distortion due to thermal interference generated as a higher density of an optical
disk that will be increasingly demanded in the future. The present embodiment is for solving this
problem, and a key point thereof is that only one side of the distribution components of the
differential metrics, where errors are generated, is targeted to calculate the signal index which
has high correlation with actual errors to be generated, and the standard deviation a of both
virtual sides distribution is determined based on this one sided distribution.
[0095] According to the present embodiment, for the specific state transition pattern which
may cause a bit error, the pattern detection section 101 according to the present embodiment
extracts specific state transition patterns (state transition patterns shown in Table 1) with which a
square of the Euclidean distance between an ideal signal of the most likely first state transition
sequence and an ideal signal of the second most likely second state transition sequence becomes
14, but the present invention is not limited to this. For example, specific state transition patterns
(state transition patterns shown in Table 2 or Table 3) with which this square of the Euclidean
distance becomes 12 may be extracted.
[0096] The optical disk controller section 15 functions as an evaluation section, which
executes evaluation processing based on the signal evaluation index M received from the
standard deviation computing section 120. This evaluation result can be displayed on a display
section, which is not illustrated, or stored in a memory as evaluation data.
[0097] In the present embodiment, the optical disk device 200 having the signal evaluation

index detection section 100 was described, but the present invention may be constructed as an
optical disk evaluation unit (reproduction signal evaluation unit) having the optical disk
controller section 15 as an evaluation section. The optical disk evaluation unit can be used for
evaluating the information recording medium before shipment, whether this information
recording medium 1 has a quality conforming to a predetermined standard or not.
[0098] The optical disk device 200 having the reproduction signal evaluation unit may be
arranged to perform the following operation. For example, the quality of the reproduction signal
is evaluated for commercial optical disks (blank disks) shipped from a factory, and optical disks
which are judged as not satisfying a predetermined quality are rejected. It is for certain possible
that optical disks recorded by a recorder (recording using a device other than this optical disk
device) can be evaluated by this optical disk device and the optical disk, which are judged as not
satisfying a predetermined quality, and are rejected.
[0099] If the optical disk device 200 can record and reproduce information, then evaluation
based on test recording can be performed before recording information on the optical disk. In
this case, the quality of reproduction signals is evaluated for the test information recorded by the
optical disk device 200, and if NG, recording conditions are adjusted until NG becomes OK, and
the optical disk is rejected if NG continues after a predetermined number of times of adjustment.
[0100] (Second Embodiment)
An optical disk device having a reproduction signal evaluation unit according to
another embodiment of the present invention will now be described with reference to the
drawings. A composing element the same as the first embodiment is denoted with a same
element number, for which description can be omitted. Fig. 2 is a block diagram depicting the
structure of the optical disk device 400 of second embodiment.
[0101] An information recording medium 1 is an information recording medium for optically
recording/reproducing information, and is an optical disk medium, for example. The optical disk
device 400 is a reproduction unit which reproduces information from the installed information
recording medium 1.
[0102] The optical disk device 400 has an optical head section 2, a pre-amplifier section 3, an
AGC (Automatic Gain Controller) section 4, a waveform equalization section 5, an A/D
conversion section 6, a PLL (Phase Locked Loop) section 7, a PR equalization section S, a
maximum likelihood decoding section 9, a signal evaluation index detection section
(reproduction signal evaluation unit) 300 and an optical disk controller section 15. The
structures and functions of these elements constituting the optical disk device 400 are the same
as the first embodiment, and descriptions thereof are omitted here.
[0103] Now a structure of the signal evaluation index detection section 300 according to the

present embodiment will be described. The signal evaluation index detection section 300. just
like the signal evaluation index detection section 100 of the first embodiment, can be used as an
evaluation unit for judging whether the quality of the information recording medium 1 conforms
to a predetermined standard before shipment. The present signal evaluation index detection
section 300 may also be installed in a drive unit of the information recording medium 1, and
used as an evaluation unit to perform test recording before a user records information on the
information recording medium 1.
[0104] The signal evaluation index detection section 300 has pattern detection sections 101,
106 and 111, differential metric computing sections 102, 107 and 112, level decision sections
103, 108 and 113, pattern count sections 104, 109 and 114, integration sections 105, 110 and 115,
error computing sections 116, 117 and 118, an adding section 119 and a standard deviation
computing section 120.
[0105] A waveform-shaped digital reproduction signal which is output from the PR
equalization section 8, and a binary signal which is output from the maximum likelihood
decoding section 9, are input to the signal evaluation index detection section 300. The pattern
detection sections 101, 106 and 111 compare the transition data sequences in Tables 1, 2 and 3
and the binary data which is output from the maximum likelihood decoding section 9
respectively. If the binary data matches the transition data sequences in Tables 1, 2 and 3 as a
result of comparison, the pattern detection sections 101, 106 and 111 select respectively a most
likely first state transition sequence and a second most likely second state transition sequence
based on Table 1, Table 2 and Table 3.
[0106] And based on the selection results of the pattern detection sections 101, 106 and 111,
the differential metric computing sections 102, 107 and 112 compute a metric, which is a
distance between an ideal value of a state transition sequence (PR equalization ideal value: see
Table 1, Table 2 and Table 3) and the digital reproduction signal. Then the differential metric
computing sections 102, 107 and 112 compute the difference between the metrics computed
from the two state transition sequences respectively, and perform the absolute value processing
on the metric differences having plus or minus values.
[0107] The outputs from the differential metric computing sections 102,107 and 112 are input
to the level decision sections 103, 108 and 113 respectively. The level decision sections 103, 108
and 113 compare differential metrics computed by the differential metric computing sections 102,
107 and 112 with a predetermined value (signal processing threshold) respectively. The pattern
count sections 104, 109 and 114 count a number of differential metrics which are not greater
than the signal processing threshold respectively. These count values each become a pattern
generation frequency when an error rate is calculated. The integration sections 105. 110 and 115

integrate the differential metrics which are not greater than the signal processing threshold
respectively. The mean value of the differential metrics which are not greater than the signal
processing threshold can be determined by dividing the integration value determined by the
integration sections 105. 110 or 115 by a number of generated patterns.
[010S] Each integration section integrates differential metrics which are not greater than the
signal processing threshold, and each computing section divides each integration value by a
number of generated patterns, so as to determine a mean value of the differential metrics which
are not greater than the signal processing threshold, but each integration section may integrate
the differential metrics which are less than the signal processing threshold, and each computing
section divides each integration value by a number of generated patterns, so as to determine a
mean value of the differential metrics which are less than the signal processing threshold.
[0109] The error computing sections 116, 117 and 118 calculate a predicted error rate from
each integration value of the differential metrics which are not greater than the signal processing
threshold and the number of generated patterns. The error rates calculated by the error
computing sections 116, 117 and 118 are added by the adding section 119. The standard
deviation corresponding to this error rate is computed by the standard deviation computing
section 120, and this becomes the signal index value to evaluate the signal quality. The process
by this signal evaluation index detection section 300 will now be described in detail.
[0110] The reproduction signal reproduced from the information recording medium 1 in the
PRML processing is output from the maximum likelihood decoding section 9 as a binary signal,
as mentioned above, and is input to the signal evaluation index detection section 300. When one
of the transition data sequence patterns in Table 1 is detected from this binary signal, the PR
equalization ideal values of the first state transition sequence and the second state transition
sequence are determined. For example, if (0, 0, 0, 0, X, 1, 1, 0, 0,) is decoded as the binary-
signal in Table 1, (S0, S1, S2, S3, S5, S6) is selected as the most likely first state transition
sequence, and (S0, S0, S1, S2, S9, S6) is selected as the second most likely second state
transition sequence. The PR equalization ideal value corresponding to the first state transition
sequence is (1. 3, 5, 6, 5). The PR equalization ideal value corresponding to the second state
transition sequence is (0, 1, 3, 4, 4).
[0111] Then the differential metric computing section 102 determines a first metric (Pb14)
which is a square of the Euclidean distance between the reproduction signal sequence (digital
reproduction signal) and the PR equalization ideal value corresponding to the first state transition
sequence. In the same way. the differential metric computing section 102 determines a second
metric (Pa14) which is a square of the Euclidean distance between the reproduction signal
sequence and the PR equalization ideal value corresponding to the second state transition

sequence. The differential metric computing section 102 determines an absolute value of the
difference of the first metric (Pbu) and the second metric (Pb14), as differential metric D14 = |
Pa14 - Pb14 |. The computing of Pb14 is shown in Expression (9). and the computing of Pa14 is
shown in Expression (10). In these expressions, bk denotes a PR equalization ideal value
corresponding to the first state transition sequence, ak denotes a PR equalization ideal value
corresponding to a second state transition sequence, and xk denotes a reproduction signal
sequence.
[0112]

[0115] In order to determine a signal evaluation index having a high correlation with the error
rate, an evaluation method considering all the patterns which have a high possibility of error
generation is required in the signal processing based on a PR12221 ML system.
[0116] Fig. 8 is a diagram depicting the distribution of differential metrics in the signal
processing of the PR 12221 ML system. In Fig. 8, the x-axis indicates a differential metric, and
the y-axis indicates a frequent) of a predetermined differential metric value. As the differential
metric (square of Euclidean distance) becomes smaller in the distribution, the possibility of
generating an error is higher in the signal processing based on the PR12221 ML system. As
shown in the graph of Fig. 8, the differential metrics have a distribution group in the sections 12
and 14. and differential metrics higher than this are 30 or more. In other words, in order to
acquire a signal index having a high correlation with the error rate, it is sufficient to target the
differential metrics in the groups 12 and 14. These groups indicate the state transition sequence
patterns shown in Table 1, Table 2 and Table 3. And the pattern detection sections 101,106 and
111 identify these state transition sequence patterns. This operation of the differential metric
computing unit, which computes the metric difference from these identified state transition
sequence patterns, will be described in more detail.
[0117] The distribution of (A) in Fig. 10 is an output frequency distribution of the differential

metric computing section 102. the distribution in (B) of Fig. 10 is an output frequency
distribution of the differential metric computing section 107. and the distribution in (C) of Fig.
10 is an output frequency distribution of the differential metric computing section 112. Fig.
10(A) shows an output frequency distribution of the differential metric computing section 102.
The processing of the differential metric computing section 107 is shown in Expression (12) to
Expression (14). and the processing of the differential metric computing section 112 is shown in
Expression (15) to Expression (17).
[0118]

[0124] The distributions of (A). (B) and (C) in Fig. 10 have a different frequency and center
position respectively. A number of error bits which are generated when each of these patterns
cause an error is also different. The state transition patterns in Table 1, where a square of the
Euclidean distance is 14. are patterns in which a 1-bit error occurs. The state transition patterns
in Table 2. where a square of the Euclidean distance is 12, are patterns in which a 2-bit error
occurs, and the state transition patterns in Table 3. where a square of the Euclidean distance is 12.

are patterns in which a 3-bit error occurs. The error patterns of which the square of the
Euclidean distance is 12, in particular, depends on the number of 2Ts that continue, and in the
case of the recording modulation codes which allow a continuation of 6 units of 2T, a maximum
6-bit error is generated. Table 3 does not cover a 6-bit error in which 2T continuously errors, but
a pattern to evaluate 2T continuous errors can be defined according to necessity, so as to
extrapolate the evaluation target pattern table.
[0125] In the state transition sequence patterns in each table, the error generation probability
in the recording modulation code sequence is also different. For example, the error generation
frequency is: the state transition sequence patterns in Table 1 are about 40% of all the samples,
the state transition sequence patterns in Table 2 are about 15% of all the samples, and the state
transition sequence patterns in Table 3 are about 5% of all the samples. In this way, the
distributions shown by (A), (B) and (C) in Fig. 10 have different weights in terms of the standard
deviation σ which indicates a dispersion, detection window (Euclidean distance), error
generation frequency and error bit count, so prediction of the error rate generated by these
distributions can also be computed considering these factors. A predicted error rate calculation
method, which is a major characteristic of the present application, will be described below.
[0126] As described in the above mentioned problem, when a predicted error rate is calculated
for each pattern group, the predicted error rate may not be able to be determined appropriately
depending on the profile of distribution. Therefore in the present embodiment, the standard
deviation a is calculated from the mean value of a portion of the distribution not greater than a
predetermined threshold (signal processing threshold), so as to determine the error rate, whereby
calculation accuracy of the predicted error rate is improved.
[0127] In Fig. 11, the area above the signal processing threshold is an area where an error
does not occur, and it is therefore unnecessary to predict an error rate. Therefore in order to
predict an error rate from the standard deviation of the differential metrics, an area not greater
than the signal processing threshold should be targeted. This error rate calculation method will
now be described. D14, D12A and D12B, which are outputs from the differential metric computing
sections 102, 107 and 112, are input to the level decision sections 103, 108 and 113, and are
compared with a predetermined value (signal processing threshold) respectively. In the present
embodiment, the signal processing threshold is set to 14 for D14 and 12 for both D12A and D12B.
[0128] If the differential metric is not greater than the signal processing threshold, the level
decision sections 103, 108 and 113 output the value, and increment the count value of the pattern
count sections 104, 109 and 114 corresponding to the respective pattern count. At the same time,
the integration sections 105, 110 and 115 integrate the differential metric that is not greater than
the signal processing threshold. And the error computing sections 116, 117 and 118 calculate the

estimated error rate from the integration value of the differential metrics that are not greater than
this signal processing threshold and the number of generated patterns. Operation of these error
computing sections 116. 117 and 118 will now be described.
[0129] The integration value determined in each integration section 105, 110 and 115 is
divided by the number of differential metrics (number of generated patterns) that are not greater
than the signal processing threshold, counted by the pattern count section 104, 109 and 114, then
a mean value of the differential metrics that is not greater than the signal processing threshold is
determined. If it is assumed that the mean value of the differential metrics that are not greater
than the signal processing threshold is M(x), a mean value of the distribution functions is µ, the
standard deviation is σn, the probability density function is f, and the distribution functions have
a normal distribution, then the absolute mean value m of the differential metrics that are not
greater than the signal processing threshold is given by the following Expression (18).
[0130]

[0131] Therefore the relationship of the standard deviation σn of the differential metrics that
are not greater than the signal processing threshold and the absolute mean value m of the
differential metrics that are not greater than the signal processing threshold is given by the

following Expression (19).
[0132]

[0133] Hence it is understood from Expression (18) and Expression (19) that in order to
calculate the standard deviation σn of the differential metrics that are not greater than the signal
processing threshold, the absolute mean value m of the differential metrics that are not greater
than the signal processing threshold is determined, and is then multiplied by about 1.253. Since
the signal processing threshold is fixed, the standard deviation σn can be calculated from the
absolute mean value m. Then the probability of error generation (error rate: bER), calculated by
the error computing sections 116, 117 and 118 respectively, can be determined by the following
Expression (20).
[0134]

[0135] Here d in Expression (20) denotes a Euclidean distance between an ideal signal of a
most likely first state transition sequence in the extraction target state transition patterns and an
ideal signal of a second most likely second state transition sequence. In the case of the present
embodiment, a square of the Euclidean distance is d142 = 14, d12A2 = 12 and d12B2 = 12.
[0136] Therefore if the standard deviations that are determined from the integration values
and integration count by Expression (19) are σ14, σ12A and Σ12B, then the predicted error rates
bER14, bER12A and bER12B, which are computed by the error computing sections 116, 117 and
118 respectively, are given by the following expressions.
[0137]


[0140] Here P14, P12A and P12B (= 0.4, 0.15, 0.05) are error generation probabilities in the
distribution components of all the channel points. An error generated in the state transition
sequence patterns in Table 1 is a 1-bit error, so the error generation probability has been
multiplied by 1, an error generated in the state transition sequence patterns in Table 2 is a 2-bit
error, so the error generation probability has been multiplied by 2, and an error generated in the
state transition sequence patterns in Table 3 is a 3-bit error, so the error generation probability
has been multiplied by 3 respectively. By adding these error rates, the error generation
probability of errors generated in all of the state transition sequence patterns in Table 1, state
transition sequence patterns in Table 2, and state transition sequence patterns in Table 3 can be
determined. If the error generation probability is bERall, bERall can be given by the following
Expression (24).
[0141]

[0142] The standard deviation computing section 120 converts the bit error rate determined by
Expression (24) into a signal index value, to make it to an index which can be handled in a
similar manner as jitter.
[0143]


[0144] Here P is a total of P14, P12A and P12B, and erfc() is an integration value of a
complementary error function. If the defining expression of the signal index M according to the
present invention is Expression (26), then the index value M can be determined by substituting
bERau, calculated by Expression (24), in Expression (25).
[0145]

[0146] In the above description, a virtual standard deviation a is calculated, and the signal
index value M is calculated from a predicted error rate using Expressions (20) to (26). However,
the calculation method for the evaluation index M according to the present embodiment is not
limited to this method, but may be determined by other defining expressions. An example of
other defining expressions will now be described.
[0147] A probability of pattern Pa to be detected as pattern Pb is assumed to be the error
function given by the following Expression (27).
[0148]

[0149] Here t in Expression (27) denotes a pattem number of Tables 1 io 3. d denotes a
Euclidean distance in each pattern group in Tables 1 to 3. Specifically, in the case of a pattern
group in Table 1, d2 is 14, and in the case of the pattern groups in Table 2 and Table 3, d2 is 12.
[0150] The error generation probability in the pattern group in Table 1, the pattern group in
Table 2. and the pattern group in Table 3 can be calculated by the following Expression (28)
using Expression (27).
[0151]

[0152] N1, N2 and N3 in Expression (28) are the generation counts of the pattern group defined

in Table 1. Table 2 and Table 3 respectively. The difference from Expression (24) is that the
error rate of each pattern group is not calculated based on all channels as a parameter, but is
based on the number of evaluation patterns in Table 1 to Table 3 as a parameter. Expression (24)
calculates an error rate of which parameter is all the channels including the evaluation patterns.
Expression (28). on the other hand, calculates the error rate of which parameter is the evaluation
patterns. When a virtual σ is calculated based on the error rates calculated by Expression (24)
and Expression (28), a same value can be calculated by considering which parameter is the target
of a. Expressions (20) to (26) are examples of computations of which parameters are all
channels. The virtual a is calculated based on Expression (28), and the evaluation index M is
calculated.
[0153] The virtual standard deviation a can be calculated by the following Expression (29).

[0155] Here E-1 denotes an inverse function of Expression (30).
[0156]

[0157] The evaluation index M can be calculated using the following Expression (31), by
normalizing with a detected window.
[0158]

[0159] In the end, Expression (26) and Expression (31) calculate a virtual σ which is
generated in the evaluation patterns defined in Table 1 to Table 3. so the index value M is
calculated as substantially a same value. The only difference is the evaluation parameter to
calculate the error rate and the notation of the detection window. Either expression may be used
to calculate the signal index value M. The calculation of the signal index value M using
Expression (31) can also be applied to the first embodiment, of which extraction target is only
specific state transition patterns.
[0160] Fig. 12 is an example of a simulation result showing the bit error rate (bER) and the
signal index value of Expression (18) when reproduction stress, such as tile, defocus and

spherical aberration, is applied. In the graph in Fig. 12. ▲ (black triangle) indicates a defocus
stress. • (black circle) indicates a spherical aberration stress. ♦ (black diamond) indicates a
radical tilt stress, and ▀ (black square) indicates a tangential tilt stress. The solid line in Fig. 12
is a theoretical curve.
[0161] Generally the criteria of the system margin is a bER of about 4.0 E-4, so the signal
index value to implement this bER is about 15 %. As shown in the graph of Fig. 12, the signal
index value M. defined in the present embodiment, is matched with the theoretical curve of the
error rate in the area of the signal index value ≤ 15 %, which is actually used in the system.
Therefore the signal evaluation method and index according to the present embodiment are very
effective in terms of evaluating signals appropriately.
[0162] As described above, according to the present embodiment, state transition sequence
patterns of merging, paths, of which Euclidean distance in the PRML signal processing is
relatively short, are targeted, and one signal evaluation index is generated based on the
differential metric information of a plurality of pattern groups, having a different error
generation probability and a different number of errors to be generated. Specifically, predicted
error rates are calculated from the mean values of the differential metric information, which are
not greater than the signal processing threshold of each pattern, the total thereof is calculated,
then a virtual standard deviation (hereafter a) of normal distribution is calculated from the total
of error rates, and the signal evaluation index, including this standard deviation a of the normal
distribution, is generated. As a result, it is possible to realize a signal evaluation method and
evaluation index having very high correlation with the error rate.
[0163] The pre-amplifier section 3, the AGC section 4 and the waveform equalization section
5 in the present embodiment in Fig. 2 may be constructed as one analog integrated circuit (1SI).
The pre-amplifier section 3, the AGC section 4, the waveform equalization section 5,,the A/D
conversion section 6, the PLL section 7, the PR equalization section 8, the maximum likelihood
decoding section 9, the signal evaluation index detection section 100, and the optical disk
controller section 15 may be consolidated as one analog-digital-mixed integrated circuit (1SI).
[0164] In each of the above mentioned embodiments, a case of using the reproduction device
as the optical disk device was described. However, needless to say, the optical disk device of the
present invention is not limited to this, but can also be applied to a reccrding'reproduction device.
In this case, circuits for recording are added, but description thereof is omitted here, since a
known circuit structure can be used.
[0165] (Third Embodiment)
An optical disk device according to still another embodiment of the present invention
will now be described with reference to the drawings.

[0166] Fig. 15 is a block diagram depicting a general structure of the optical disk device of the
present embodiment.
[0167] The optical disk device 600 has: an optical head section 2, a pre-amplifier section 3. an
AGC (Automatic Gain Controller) section 4, a waveform equalization section 5, an A/D
conversion section 6, a PLL (Phase Locked Loop) section 7, a PR equalization section S, a
maximum likelihood decoding section 9, a signal evaluation index detection section
(reproduction signal evaluation unit) 500 and an optical disk controller section 15. The
structures and functions of these composing elements constituting the optical disk device 600 are
the same as the first embodiment, and descriptions thereof are omitted here.
[0168] The optical disk device 600 according to the present embodiment has a signal
evaluation index detection section 500 as the reproduction signal evaluation unit. The signal
evaluation index detection section 500 has the same structure as the signal evaluation index
detection section 100 of the first embodiment, except for the setting of the signal processing
threshold. Hence composing elements having a similar structure and function as the signal
evaluation index detection section 100 of the first embodiment are denoted with a same symbol,
and description thereof is omitted.
[0169] As shown in Fig. 13, the signal evaluation index detection section 500 has a mean
value computing section 121 for computing a mean value of outputs of the differential metric
computing section 102, in addition to the structure of the first embodiment.
[0170] Now the operation of the mean value computing section 121 and how to set the signal
processing threshold will be described. In the first embodiment, a predetermined value, that is, a
code distance of ideal signals (a square of Euclidean distance between an ideal signal of a most
likely first state transition sequence and an ideal signal of a second most likely second state
transition sequence in a specific extraction target state transition pattern) is used as the signal
processing threshold. This is because in optimized recording, the mean value of outputs of the
differential metric computing section matches the code distance of the ideal signals. However,
as recording densities of optical disks further improve, recording optimization, to match the
mean value with the code distance of the ideal signals, may not be possible in some cases.
[0171] Therefore the signal evaluation index detection section 500 of the present embodiment
has the mean value computing section 121 for computing a mean value of outputs of the
differential metric computing section 102, and inputs this mean value to the level decision
section 103 as the signal processing threshold.
[0172] According to the foregoing structure, the signal processing threshold can be
appropriately set at the center of distribution, which is output from the differential metric
computing section 102. Thereby correlation of the signal index value and the bit error rate, when

the recording density is increased, can be improved compared with the structure of the first
embodiment.
[0173] Therefore the structure of the present embodiment, using the mean value of the
differential metric distribution as the signal processing threshold, is particularly useful when a
high density recording medium is adopted as the information recording medium 1.
[0174] (Fourth Embodiment)
An optical disk device according to still another embodiment of the present invention
will now be described with reference to the drawings.
[0175] Fig. 14 is a block diagram depicting the general structure of the optical disk device of
the present invention.
[0176] The optical disk device 800 has an optical head section 2, a preamplifier section 3, an
AGC (Automatic Gain Controller) section 4, a waveform equalization section 5, an AID
conversion section 6, PLL (Phase Locked Loop) section 7. a PR equalization section 8, a
maximum likelihood decoding section 9, a signal evaluation index detection section
(reproduction signal evaluation unit) 700 and an optical disk controller section 15. The structure
and function of these composing elements constituting the optical disk device 800 are the same
as the second embodiment, so description thereof is omitted here.
[0177] The optical disk device 800 according to the present embodiment has a signal
evaluation index detection section 700 as the reproduction signal evaluation unit 700. The signal
evaluation index detection section 700 has the same structure as the signal evaluation index
detection section 300 of the second embodiment, except for the setting of the signal processing
threshold. Hence a composing element having a similar structure and function as the signal
evaluation index detection section 300 of the second embodiment is denoted with a same symbol,
and description thereof is omitted.
[0178] As shown in Fig. 14, the signal evaluation index detection section 700 has mean value
computing sections 121, 122 and 123 for computing a respective mean value of the outputs of
the differential metric computing sections 102, 107 and 112, in addition to the structure of the
second embodiment.
[0179] Now operation of the mean value computing sections 121. 122 and 123, and how to set
the signal processing threshold will be described. In the third embodiment, a predetermined
value, that is, a code distance of ideal signals (a square of Euclidean distance between an ideal
signal of a most likely first state transition sequence and an ideal signal of a second most likely
second state transition sequence in each extraction target state transition pattern), is used as the
signal processing threshold. This is because in optimized recording, the mean value of outputs
of the differential metric computing section matches the code distance of ideal signals. However,

as the recording densities of optical disks further improve, recording optimization to match the
mean value with the code distance of the ideal signals may not be possible in some cases.
[0180] Therefore the signal evaluation index detection section 700 of the present embodiment
has the mean value computing sections 121. 122 and 123 for computing a respective mean value
of outputs of the differential metric computing sections 102. 107 and 112, and inputs this mean
value to the level decision sections 103. 108 and 113 as the signal processing threshold
respectively.
[0181] According to the foregoing structure, the signal processing threshold can be
appropriately set at the center of distribution, which is output from each of the differential metric
computing sections 102, 107 and 112. Thereby correlation of the signal index value and the bit
error rate when the recording density is increased can be improved compared with the structure
of the first embodiment.
[0182] Therefore the structure of the present embodiment, using the mean value of the
differential metric distribution as the signal processing threshold, is particularly useful when a
high density recording medium is adopted as the information recording medium 1.
[0183] (Fifth embodiment)
An optical disk device according to the fifth embodiment will now be described. Fig.
15 is a block diagram depicting the general structure of the optical disk device of the fifth
embodiment of the present invention.
[0184] The optical disk device 920 has an optical head section 2, a preamplifier section 3, an
AGC section 4, a waveform equalization section 5, an A/D conversion section 6, a PLL section 7,
a PR equalization section 8, a maximum likelihood decoding section 9, a signal evaluation index
detection section (reproduction signal evaluation unit) 910, and an optical disk controller section
15. The structure and function of part of these composing elements constituting the optical disk
device 920 are the same as the first to fourth embodiments, so descriptions thereof are omitted
here.
[0185] The optical disk device 920 according to the fifth embodiment has a signal evaluation
index detection section 910 as the reproduction signal evaluation unit. The signal evaluation
index detection section 910 of the fifth embodiment has the same structure as the signal
evaluation index detection section of the first and third embodiments, other than the computing
processing for determining the standard deviation of the differential metrics. Hence a composing
element, having a similar structure and function as the signal evaluation index detection section
100 of the first embodiment is denoted with a same symbol and descriptions thereof, are omitted.
[0186] Now a structure of the signal evaluation index detection section 910 according to the
fifth embodiment will be described. The signal evaluation index detection section 910. just like

the signal evaluation index detection section of the first to fourth embodiments, can be used as a
reproduction signal evaluation unit for judging whether the quality of the information recording
medium 1 conforms to a predetermined standard before shipment. The present signal evaluation
index detection section 910 may also be installed in a drive unit of the information recording
medium 1, and be used as an evaluation unit to perform test recording before a user records
information on the information recording medium 1.
[0187] The signal evaluation index detection section 910 has a pattern detection section 101, a
differential metric computing section 102, a level decision section 103, a pattern count section
104, an integration section 105, an error computing section 116, a pattern count section 124, an
integration section 125 and a standard deviation computing section 120.
[0188] As Fig. 15 shows, the signal evaluation index detection section 910 has the integration
section 125 for computing the mean value of the outputs of the differential metric computing
section 102 and the pattern count section 124 for counting the outputs of the differential metric
computing section 102, in addition to the configuration of the first embodiment.
[0189] The signal evaluation index detection section 910 evaluates the quality of a
reproduction signal reproduced from an information recording medium based on a binary signal
generated from the reproduction signal using a PRML signal processing system. The pattern
detection section 101 extracts, from the binary signal, a specific state transition pattern which has
the possibility of causing a bit error.
[0190] The differential metric computing section 102 computes a differential metric, which is
a difference of a first metric between an ideal signal of a most likely first state transition
sequence corresponding to the binary signal and the reproduction signal, and a second metric
between an ideal signal of a second most likely second state transition sequence corresponding to
the binary signal and the reproduction signal, based on the binary signal of the state transition
pattern extracted by the pattern extraction section.
[0191] The integration section 125 integrates the differential metric computed by the
differential metric computing section 102. The pattern count section 124 counts a number of
times of integration processing by the integration section 125 by counting a number of times of
pattern generation in the pattern detection section 101.
[0192] The level decision section 103 extracts the differential metric which is not greater than
a predetermined signal processing threshold. The integration section 105 integrates the
differential metric which is not greater than the signal processing threshold extracted by the level
decision section 103. The pattern count section 104 counts a number of times of integration
processing by the integration section 105.
[0193] The error computing section 116 computes an error rate that is predicted based on the

integration value that is integrated by the integration section 125. the count value that is counted
by the pattern count section 124, the integration value that is integrated by the integration section
105, and the count value that is counted by the pattern count section 104.
[0194] The error computing section 116 also computes an error rate based on a mean value of
the differential metrics computed based on the integration value that is integrated by the
integration section 125 and the count value that is counted by the pattern count section 124, and
a predetermined computing result based on the integration value that is integrated by the
integration section 105 and the count value that is counted by the pattern count section 104.
[0195] The error rate computing section 116 also computes a standard deviation of a
differential metric of which differential metric output is the mean value or less, using a linear
expression of which arguments are the integration value that is integrated by the integration
section 125, the count value that is counted by the pattern count section 124, the integration
value that is integrated by the integration section 105 and the count value that is counted by the
pattern count section 104, and computes the error rate based on the standard deviation. The
linear expression is an approximate expression that is computed using iteration based on the
Newton's method.
[0196] The standard deviation computing section 120 computes a standard deviation based on
the error rate that is computed by the error rate computing section 116.
[0197] The pattern count section 124 counts the number of times of generation of a specific
pattern that is detected by the pattern detection section 101 and outputs the count value N1. The
integration section 125 integrates the output from the differential metric computing section 102
and outputs the integration value S1. The integration section 105 integrates the output result by
the level decision section 103 and outputs the integration value JS1. The pattern count section
104 counts a number of time when the conditions are met in the level decision section 103, and
outputs the count value JN1. The structures of the composing elements, other than the
integration section 125 and the pattern count section 124 for computing the mean value of the
differential metrics of each pattern group, are the same as the first embodiment, and the
descriptions thereof are omitted here.
[019S] In the fifth embodiment, the signal evaluation index detection section 910 corresponds
to an example of the reproduction signal evaluation unit, the pattern detection section 101
corresponds to an example of the pattern extraction section, the differential metric computing
section 102 corresponds to an example of the differential metric computing section, the
integration section 125 corresponds to an example of the first integration section, the pattern
count section 124 corresponds to an example of the first count section, the level decision section
103 corresponds to an example of the differential metric extraction section, the integration

section 105 corresponds to an example of the second integration section, the pattern count
section 104 corresponds to an example of the second count section, the error computing section
116 corresponds to an example of the error rate computing section, and the standard deviation
computing section 120 corresponds to an example of the standard deviation computing section.
[0199] Now the computation of the standard deviation of the differential metric for computing
the predicted error rate according to the fifth embodiment will be described. The structure
proposed by the third and fourth embodiments is where a mean value of the differential metrics
is determined when the mean value of the outputs from the differential metric computing section
does not match with the code distance of the ideal signal depending on the recording state
(quality), and a predicted error rate is computed from the standard deviation of the differential
metric that is determined based on this mean value, so as to improve the correlation of an
actually generated error rate and the signal index value.
[0200] With this configuration of the third and fourth embodiments, however, two problems
can occur. One, since the mean value of the differential metrics in the signal index value
measurement area is determined, the mean value must be determined in advance. In order to
compute the signal index value, a plurality of times of measurements are required, and this
processing may take time. Two, is that a possible solution for the first problem is to update the
mean value while computing the mean value. However the optimum value of the response
characteristic of computing the mean value could differ in some cases depending on the
recording state. Therefore it is difficult to unequivocally determine the response characteristic of
computing the mean value for obtaining compatible signal index values.
[0201] In order to solve these problems, the fifth embodiment suggests a calculation method
that uses a predetermined fixed value called the "code distance of an ideal signal" as the signal
processing threshold in the processing for determining a standard deviation from the output of
the differential metric, just like the first embodiment. Then if the mean value of the outputs of
the differential metric computing section 102 does not match with the code distance of the ideal
signal depending on the recording state (quality), an error of the standard deviation, that is
generated by a shift in the mean value, is corrected so that the insufficient correlation between
the signal index value and the bit error rate is solved.
[0202] Fig. 16A and Fig. 16B are diagrams depicting a distributions in the differential metric
ranges in a specific recording state. The distributions in Fig. 16A and Fig. 16B are examples
when the mean value of the outputs of the differential metrics does not match with the code
distance of the ideal signal. Fig. 16A is a diagram depicting distribution in the differential metric
range used for determining the standard deviation in the thrid and fourth embodiments. The
third and fourth embodiments determine the mean value of the distribution, then determine the

standard deviation from the differential metric values in a range smaller than the mean value, and
compute the predicted error rate, whereby the reproduction signal evaluation method that does
not depend on the recording quality can be provided. The fifth embodiment, on the other hand,
determines the standard deviation using a fixed signal processing threshold, so as to obtain an
effect similar to the third and fourth embodiments.
[0203] Fig. 16B is a diagram depicting a distribution in the differential metric range that is
used for determining the standard deviation in the fifth embodiment. According to the fifth
embodiment, a predetermined correction is performed on the differential metric values in a range
smaller than a fixed signal processing threshold, whereby the standard deviation corresponding
to the third and fourth embodiments can be determined.
[0204] Now a method for calculating the standard deviation according to the fifth embodiment
will be described. First the parameters to be used for the calculation in the fifth embodiment are
redefined. S1 is an integration value of the differential metrics, N1 is a frequency of the
differential metrics (count value to indicate a number of times of integration of S1), JS1 is an
integration value of the differential metrics which are not greater than the signal processing
threshold ("0" in this case), JN1 is a frequency of differential metrics which are not greater than
the signal processing threshold ("0" in this case) (count value to indicate a number of times of
integration of JS1), α is a predetermined frequency coefficient, and E1 is an ideal signal
processing value.
[0205] Fig. 17A and Fig. 17B are diagrams depicting the calculation method for the standard
deviation according to the fifth embodiment. In order to determine a virtual standard deviation
σ1' from the integration value JS1 indicated by the hatched portion shown in Fig. 17A, the
integration value JS1 must be normalized by the count value N1. According to the fifth
embodiment, a virtual standard deviation is determined for the pattern group in Table 1. E1
indicates a detection window, and 14 is inserted for the pattern group in Table 1.
[0206] The count value N1 can be determined by the following Expression (32).
[0207]

[0208] The count value JN; can be determined by the following Expression (33).
[0209]


[0210] The mean value µ of the distributions of Expression (32) and Expression (33) is
defined by the following Expression (34).
[0211]

[0212] The calculation for normalizing the integration value JS1 by the count value N1 is
given by the following Expression (35).
[0213]

[0214] In other words, the above Expression (35) can be transformed into the following
Expression (36).
[0215]

[0216] Using two variables, which are a1 given by the following Expression (37) and b1 given
by the following Expression (38). the Expression (36) can be simplified into the following
Expression (39).
[0217]


[0220] A conversion for determining the standard deviation σ1 considering the shift of the
mean value of the distribution based on the variable a1 given by Expression (37) and the variable
b1 given by Expression (38) (Fig. 17B) will be described next.
[0221] The above Expression (39), which is defined as a function of which arguments are the
standard deviation σ1 and variable b1, can be given by the following Expression (40).
[0222]

[0223] If the virtual standard deviation σ1' shown in Fig. 17A is moved to the left side of the
Expression (40), the following Expression (41) is established.
[0224]

[0225] The index, in which the shift of the mean value of the distribution shown in Fig. 17B is
reflected in the detection window, can be defined as the following Expression (42).
[0226]


[0227] σ1/2 for the two variables a1 and b1, that satisfies the above Expression (42). is
calculated by the Newton's method. The Newton's method is one of the algorithms for solving
equations based on iteration, which is used for solving equations by numerical calculation in the
numerical analysis field, and has been used for numerical calculation for a long time. Here
descriptions on the algorithm of the Newton's method are omitted.
[0228] Fig. 18 is a graph depicting a relationship of a variable a1(ax) and standard deviation
σ1/2(σx/2) computed by a Newton's method, which is shown for each shift amount of the mean
value of the outputs of the differential metrics. In Fig. 18, the horizontal axis indicates variable
a1(ax) [%], which is determined by the above Expression (37), and the vertical axis indicates σ1/2
(σx/2) [%] that is calculated by a Newton's method. The shift amount of the mean value of the
outputs of the differential metrics is a variable b1 that is determined by the above Expression (38).
The relationship of σ1/2 [%] and the variable a1 shown in Fig. 18 can be expression by a linear
expression of which variable is b1. Hence σ1/2, which is determined by the Newton's method,
can be expression by the linear expression of which variable b1 is the mean value of the outputs
of the differential metrics.
[0229] In the linear expression of Expression (42), P denotes an inclination when the mean
value of the outputs of the differential metrics is the variable b1, and Q denotes an intercept when
the mean value of the outputs of the differential metrics is the variable b1. The value of P and the
value of Q may be stored in a table with respect to b1 determined by approximate calculation.
Table 4 shows a concrete example of a table which indicates the value P and the value Q of
which argument is the variable bx. x in the variable bx in Table 4 contributes determining the
standard deviation σx respectively for pattern groups in Table 1, Table 2 and Table 3, and values
"1", "2" or "3" corresponding to Table 1, Table 2 or Table 3 is inserted respectively.
[0230] [Table 4]

[0231] In the example in Table 4. the correction table is unequivocally defined in a -30% to
+30% correction range, but the correction range may be widened or narrowed. For the
correction range, it is preferable to support the range considering the actually generated shift
amount. The argument bx of P(bx) and Q(bx) in Table 4 is indicated with 0.05 spacing as an

example. If a value in the space of the variables bx which are stored in advance (e.g. 0.025) is
input here as the variable bx, then P(bx) and Q(bx) corresponding to the variables bx before and
after the input value, out of the variables bx stored with 0.05 spacing in Table 4. may be linearly
interpolated respectively. P(bx) and Q(bx) corresponding to the variable bx closest to the input
value may also be selected out of the variable bx stored in advance.
[0232] In this way. according to the fifth embodiment, correction computing for determining
the standard deviation σ1/2, considering the shift of the mean value of the distribution, is
performed using the integration value (JS1) and a number of times of integration (JN1) based on
the shift amount (S1/Ni) of the distribution of the outputs of the differential metric computing
section 102 and the fixed signal processing threshold. In the fifth embodiment, a simple linear
expression given by the Expression (42) is used for the correction expression to improve the
predicted error rate computing accuracy.
[0233] In the fifth embodiment, the predicted error rate is determined using the standard
deviation σ1/2 that is determined by the Expression (42) according to one of the patterns in Table
1 to Table 3. Therebv a signal index value which has a high correlation with the error rate can be
determined even if the center of the distribution of the outputs of the differential metric
computing section 102 is shifted from the signal processing threshold as in Fig. 21B and Fig.
21C.
[0234] (Sixth embodiment)
An optical disk device having a reproduction signal evaluation unit according to the
sixth embodiment of the present invention will now be described with reference to the drawings.
A composing element that is the same as the fifth embodiment is denoted with a same element
number for which description can be omitted. Fig. 19 is a block diagram depicting the structure
of the optical disk device 940 of the sixth embodiment.
[0235] An information recording medium 1 is an information recording medium for optically
recording/reproducing information, and is an optical disk medium, for example. The optical disk
device 940 is a reproduction unit which reproduces information from the installed information
recording medium 1.
[0236] The optical disk device 940 has an optical head section 2, a preamplifier section 3, an
AGC section 4. a waveform equalization section 5. an AT) conversion section 6. a PLL section 7,
a PR equalization section 8, a maximum likelihood decoding section 9, a signal evaluation index
detection section (reproduction signal evaluation unit) 930, and an optical disk controller section
15. The structures and functions of a part of these elements constituting the optical disk device
940 are the same as the first to fifth embodiments, and descriptions thereof are omitted here.
[0237] Now a structure of the signal evaluation index detection section 930 according to the

sixth embodiment will be described. The signal evaluation index detection section 930. just like
the signal evaluation index detection section of the first to fifth embodiments, can be used as a
reproduction signal evaluation unit forjudging whether the quality of the information recording
medium 1 conforms to a predetermined standard before shipment. The present signal evaluation
index detection section 930 may also be installed in a drive unit of the information recording
medium 1. and used as an evaluation unit to perform test recording before a user records
information on the information recording medium 1.
[023S] The signal evaluation index detection section 930 has pattern detection sections 101,
106 and 111, differential metric computing sections 102, 107 and 112, level decision sections
103, 108 and 113, pattern count sections 104, 109 and 114, integration section 105,110 and 115,
error computing sections 116.117 and 118, pattern count sections 124. 126 and 128, integration
sections 125,127 and 129, an addition section 119, and a standard deviation computing section
120.
[0239] In addition to the structure in the first embodiment, the signal evaluation index
detection section 930 has integration sections 125, 127 and 129 for computing a mean value of
the outputs of the differential metric computing sections 102,107 and 112, and pattern count
sections 124,126 and 128 for counting the output of the differential metric computing section
102, as shown in Fig. 19.
[0240] The pattern detection sections 101,106 and 111 extract, from the binary signal, a state
transition pattern which has the possibility of causing a bit error respectively. The differential
metric computing sections 102,107 and 112 calculate a differential metric, which is a difference
of the first metric between an ideal signal of a most likely first state transition sequence
corresponding to the binary signal and the reproduction signal, and a second metric between an
ideal signal of a second most likely second state transition sequence corresponding to the binary-
signal and the reproduction signal based on the binary signal for each state transition pattern
extracted by the pattern detection sections 101,106 and 111 respectively.
[0241] The integration sections 125, 127 and 129 integrate the differential metric computed by
the differential metric computing sections 102,107 and 112 for each state transition patterns
respectively. The pattern count sections 124, 126 and 128 count a number of times of integration
processing by the integration sections 125,127 and 129 for each state transition pattern
respectively.
[0242] The level decision sections 103,10S and 113 extract differential metrics not greater
than a predetermined signal processing threshold for each state transition pattern respectively.
The integration sections 105. 110 and 115 integrate the differential metrics which are not greater
than the signal processing threshold extracted for each state transition pattern by the level

decision section 103. 108 and 113 respectively. The pattern count sections 104, .109 and 114
count the number of times of integration processing by the integration sections 105. 110 and 115
for each state transition pattern respectively.
F02431 The error computing sections 116. 117 and 118 calculate a plurality of error rates
predicted based on the plurality of integration values integrated by the integration sections 125.
127 and 129, the plurality of count values counted by the pattern count sections 124,126 and 12S,
the plurality of integration values integrated by the integration sections 105, 110 and 115. and the
plurality of count values counted by the pattern count sections 104. 109 and 114 for each state
transition pattern respectively.
[0244] The standard deviation computing section 120 computes a standard deviation based on
the total of the plurality of error rates that are computed by the error computing sections 116,117
and 118.
[0245] The pattern count sections 124, 126 and 128 count a number of times of generation of
a specific pattern detected by the pattern detection sections 101.106 and 111, and output the
count values N1, N2 and N3 respectively. The integration sections 125,127 and 129 integrate the
outputs of the differential metric computing sections 102,107 and 112, and output the
integration values S1, S2 and S3 respectively. The integration sections 105,110 and 115 integrate
the output results of the level decision sections 103, 108 and 113, and output the integration
values JS1, JS2 and JS3 respectively. The pattern count sections 104,109 and 114 count a
number of times the conditions are met in the level decision sections 103,108 and 113, and
output the count values JN1, JN2 and JN3 respectively. A structure other than the integration
sections 125,127 and 129 for computing a mean value of the differential metrics of each pattern
group and the pattern count sections 124,126 and 128 are exactly the same as the first
embodiment, and detailed descriptions thereof are therefore omitted.
[0246] In the sixth embodiment, the signal evaluation index detection section 930 corresponds
to the example of the reproduction signal evaluation unit, the pattern detection sections 101,106
and 111 correspond to an example of the pattern extraction section, the differential metric
computing sections 102.107 and 112 correspond to an example of the differential metric
computing section, the integration sections 125. 127 and 129 correspond to an example of the
first integration section, the pattern count sections 124, 126 and 128 correspond to an example fo
the first count section, the level decision sections 103, 10S and 113 correspond to an example of
the differential metric extraction section, the integration sections 105, 110 and 115 correspond to
an example of the second integration section, the pattern count sections 104, 109 and 114
correspond to an example of the second count section, the error computing sections 116. 117 and
118 correspond to an example of the error rate computing section, and the standard deviation

computing section 120 corresponds to an example of the standard deviation computing section.
[0247] Now the computation of the standard deviation of the differential metrics for
computing the predicted error rate according to the sixth embodiment will be described. The
structure proposed by the third and fourth embodiments is where a mean value of the differential
metrics is determined when the mean value of the outputs from the differential metric computing
section does not match with the code distance of the ideal signal depending on the recording
state (quality), and a predicted error rate is computed from the standard deviation of the
differential metric that is determined based on this mean value, so as to improve the correlation
of an actually generated error rate and the signal index value.
[024S] With this configuration of the third and fourth embodiments, however, two problems
can occur. One, since the mean value of the differential metrics in the signal index value
measurement area is determined, the mean value must be determined in advance. In order to
compute the signal index value, a plurality of times of measurements are required, and this
processing may take time. Two, is that a possible solution for the first problem is to update the
mean value while computing the mean value. However, the optimum value of the response
characteristic of computing the mean value could differ in some cases according to the recording
state. Therefore it is difficult to unequivocally determine the response characteristic of
computing the mean value for obtaining compatible signal index values.
[0249] In order to solve these problems, the sixth embodiment suggests a calculation method
that uses a predetermined fixed value called the "code distance of an ideal signal" as the signal
processing threshold in the processing for determining a standard deviation from the output of
the differential metric, just like the first embodiment. Then if the mean value of the outputs of
the differential metric computing sections 102,107 and 112 does not match with the code
distance of the ideal signal depending on the recording state (quality), an error of the standard
deviation that is generated by a shift of the mean value is corrected so that the insufficient
correlation of the signal index value and the bit error rate is solved.
[0250] Now a method for calculating the standard deviation according to the sixth
embodiment will be described. First the parameters to be used for the calculation in the sixth
embodiment are redefined. Sxis an integration value of the differential metrics, Nx is a
frequency of the differential metrics (count value to indicate a number of times of integration of
Sx), JSx is an integration value of the differential metrics which are not greater than the signal
processing threshold ("0" in this case). JNx is a frequency of the differential metrics which are
not greater than a signal processing threshold ("0" in this case) (count value to indicate a number
of times of integration of JSx). a is a predetermined frequency coefficient, and Ex is an ideal
signal processing value.

[0251] Fig. 20A and Fig. 20B are diagrams depicting the calculation method for the standard
deviation according to the sixth embodiment. In order to determine a virtual standard deviation
σx from the integration value JSX indicated by the hatched portion shown in Fig. 20A. the
integration value JSX must be normalized by the count value N x means to determine a virtual
standard deviation for the pattern groups in Tables 1, 2 and 3 respectively. One of the values "1"'.
"2" and "3" corresponding to Table 1, Table 2 and Table 3 is inserted in x. Ex indicates a
detection window, and "14" is inserted for the pattern group in Table 1, and "12" is inserted for
the pattern groups in Table 2 and Table 3.
[0252] The count value Nx can be determined by the following Expression (43).
[0253]

[0254] The count value JNX can be determined by the following Expression (44).
[0255]

[0256] The mean value µ of the distributions of Expression (43) and Expression (44) is
defined by the following Expression (45).
[0257]

[025S] The calculation for normalizing the integration value JSX by the count value Nx is
given by the following Expression (46).
[0259]


[0260] In other words, the above Expression (46) can be transformed into the following
Expression (47).
[0261]

[0262] Using two variables, ax given by the following Expression (48) and bx given by the
following Expression (49), Expression (47) can be simplified into the following Expression (50).
[0263]

[0266] A conversion table for determining the standard deviation σx considering the shift of

the mean value of the distribution based on ax given by Expression (48) and the variable bx given
by Expression (49) (Fig. 20B) will be described next.
[0267] The above Expression (50), which is defined as a function of which arguments are the
standard deviation σx and variable bx, can be given by the following Expression (51).
[0268]

[0269] If the virtual standard deviation σx' shown in Fig. 20A is moved to the left side of the
Expression (51), the following Expression (52) is established.
[0270]

[0271] The index in which the shift of the mean value of the distribution shown in Fig. 20B is
reflected to the detection window can be defined as the following Expression (53).
[0272]

[0273] σx/2 for the two variables, ax and bx, that satisfy the above Expression (53), is
calculated by the Newton's method. The Newton's method is one of the algorithms for solving
equations based on iteration which is used for solving equations by numerical calculation in the
numerical analysis field, and has been used for numerical calculation for a long time. Here
descriptions on algorithm of the Newton's method are omitted.
[0274] As described in Fig. 18, the shift amount of the mean value of the outputs of the
differential metrics is a variable bx that is determined by the above Expression (49). The
relationship of σx/2 [%] and the variable ax shown in Fig. 18 can be expressed by a linear
expression of which variable is bx. Hence σx/2 which is determined by the Newton's method can
be expressed by the linear expression of which variable bx is the mean value of the outputs of the
differential metrics.
[0275] In the linear expression of the above Expression (53), P denotes an inclination when
the mean value of the outputs of the differential metrics is the variable bx, and Q denotes an
intercept when the mean value of the outputs of the differential metrics is the variable bx. The

value of P and the value of Q may be stored in the table with respect to bx determined by
approximate calculation. In other words, the standard deviation computing section 120 may be
stored in a table in advance, which indicates the value of P and the value of Q of which argument
is the variable bx, shown in Table 4.
[0276] In this way, according to the sixth embodiment, correction computing for determining
the standard deviation σx/2 considering the shift of the mean value of the distribution is
performed using the integration value (JSX) and a number of times of integration (JNX) based on
the shift amount (Sx/Nx) of the distribution of the outputs of the differential metric computing
sections 102,107 and 112 and the fixed signal processing threshold. In the sixth embodiment, a
simple linear expression given by the Expression (53) is used for the correction expression to
improve the predicted error rate computing accuracy.
[0277] In the sixth embodiment, the predicted error rate is determined using the standard
deviation σx/2 that is determined by the Expression (53) according to the pattern groups in Table
1 to Table 3. Thereby a signal index value which has a high correlation with the error rate can be
determined even if the center of the distribution of the outputs of the differential metric
computing sections 102,107 or 112 is shifted from the signal processing threshold as in Fig. 21B
and Fig. 21C.
[0278] The above embodiments mainly include the invention having the following structures.
[0279] The reproduction signal evaluation method according to an aspect of the present
invention is a reproduction signal evaluation method for evaluating a quality of a reproduction
signal reproduced from an information recording medium based on a binary signal generated
from the reproduction signal using a PRML signal processing system, the method comprising: a
pattern extraction step of extracting, from the binary signal, a specific state transition pattern
which has a possibility of causing a bit error; a differential metric computing step of computing a
differential metric, which is a difference of a first metric between an ideal signal of a most likely
first state transition sequence corresponding to the binary signal and the reproduction signal, and
a second metric between an ideal signal of a second most likely second state transition sequence
corresponding to the binary signal and the reproduction signal, based on the binary signal of the
state transition pattern extracted in the pattern extraction step; a first integration step of
integrating the differential metric computed in the differential metric computing step; a first
count step of counting a number of times of integration processing in the first integration step; a
differential metric extraction step of extracting the differential metric not greater than a
predetermined signal processing threshold; a second integration step of integrating the
differential metric not greater than the signal processing threshold extracted in the differential
metric extraction step: a second count step of counting a number of times of integration

processing in the second integration step: an error rate computing step of computing an error rate
predicted based on an integration value that is integrated in the first integration step, a count
value that is counted in the first count step, an integration value that is integrated in the second
integration step, and a count value that is counted in the second count step; a standard deviation
computing step of computing a standard deviation based on the error rate that is computed in the
error rate computing step: and an evaluation step of evaluating a quality of the reproduction
signal using the standard deviation computed in the standard deviation comnuting sten.
[0280] According to the foregoing structure, specific state transition patterns which have the
possibility of causing a bit error are extracted from the binary signals generated by reproducing
the information recording medium. Here the state transition pattern, which has a possibility of
causing a bit error, is a state transition pattern having merging paths which could take a plurality
of state transitions when a predetermined state at a certain time transits to a predetermined state
at another time, and is a state transition pattern of merging paths of which Euclidean distance
between an ideal signal of a most likely first state transition sequence and an ideal signal of a
second most likely second state transition sequence is relatively short. If there are a plurality of
state transition patterns which have the possibility of causing a bit error, a specific state
transition pattern is selectively extracted.
[0281] Targeting the binary signal of the extracted specific state transition pattern, a
differential metric, which is a difference of a first metric between an ideal signal of a most likely
first state transition sequence corresponding to this binary signal and the reduction signal, and a
second metric between an ideal signal of a second most likely second state transition sequence
corresponding to this binary signal and the reproduction signal, is computed.
[0282] Then the calculated differential metric is integrated, and a number of times of
integration processing of the differential metrics is counted. Differential metrics which are not
greater than a predetermined signal processing threshold are extracted, and the extracted
differential metrics which are not greater than the signal processing threshold are integrated, and
a number of times of integration processing of differential metrics which are not greater than the
signal processing threshold are counted.
[0283] Then an error rate predicted based on the computed integration value of the differential
metrics, the count value of a number of times of integration processing of the differential metrics,
and the integration value of the differential metrics which are not greater than the predetermined
signal processing threshold and the count value of a number of times of integration processing of
the differential metrics which are not greater than the predetermined signal processing threshold
are computed. Further the standard deviation is computed based on the computed error rate, and
the quality of the reproduction signal is evaluated using the computed standard deviation.

[0284] Therefore when the mean value of the differential metrics does not match with the
code distance of the ideal signal depending on the recording state, an error of the standard
deviation, which is generated by a shift of the mean value of the differential metrics from the
code distance of the ideal signal, is corrected using the computed integration value of the
differential metrics, and the count value of a number of times of integration processing of the
differential metrics, whereby correlation of the error rate and the signal index value is improved
and the quality of the reproduction signal reproduced from the information recording medium,
can be evaluated at high accuracy.
[0285] In the foregoing reproduction signal evaluation method, it is preferable that the signal
processing threshold is a square of a Euclidian distance between the ideal signal of the most
likely first state transition sequence and the ideal signal of the second most likely second state
transition sequence.
[02S6] According to the foregoing structure, the signal processing threshold, corresponding to
the extraction target specific state transition patterns, can be accurately set so as to match with
the Euclidian distance between the ideal signal of the first state transition sequence and the ideal
signal of the second state transition sequence. This is particularly effective to evaluate signals
where a plurality of state transition patterns, which have the possibility of generating an error,
are mixed.
[0287] In the foregoing reproduction signal evaluation method, it is preferable that the error
rate computing step computes a standard deviation of differential metrics not greater than a mean
value of differential metric outputs, using a linear expression of which arguments are the
integration value that is integrated in the first integration step, the count value that is counted in
the first count step, the integration value that is integrated in the second integration step, and the
count value that is counted in the second count step, and computes the error rate based on the
standard deviation.
[0288] According to the foregoing structure, the standard deviation of the differential metrics
not greater than the mean value of the differential metric outputs is computed using a linear
expression of which arguments are the computed integration value of the differential metrics, the
count value of a number of times of integration processing of the differential metrics, the
integration value of the differential metrics which are not greater than the predetermined signal
threshold and the count value of a number of times of integration processing of the differential
metrics which are not greater than the predetermined signal processing threshold, and the error
rate can be computed based on the standard deviation.
[0289] In the foregoing reproduction signal evaluation method, it is preferable that the linear
expression is an approximate expression that is calculated usins iteration based on the Newton's

method. According to the foregoing structure, the linear expression used for computing the error
rate can be given by an approximate expression that is computed using iteration based on the
Ne\vton*s method.
[0290] In the foregoing reproduction signal evaluation method, it is preferable that the error
rate computing step computes the error rate based on the mean value of the differential metrics
computed based on the integration value that is integrated in the first integration step and the
count value that is counted in the first count step, and a predetermined computing result based on
the integration value that is integrated in the second integration step and the count value that is
counted in the second count step.
[0291] According to the foregoing structure, the error rate can be computed based on the mean
value of the differential metrics computed based on the computed integration value of the
differential metric and the count value of a number of times of integration processing of
differential metrics, and a predetermined computing result based on the integration value of the
differential metrics which are not greater than the predetermined signal processing threshold and
the count value of a number of times of integration processing of the differential metrics which
are not greater than the predetermined signal processing threshold.
[0292] The reproduction signal evaluation method according to another aspect of the present
invention is a reproduction signal evaluation method for evaluating a quality of a reproduction
signal reproduced from an information recording medium based on a binary signal generated
from the reproduction signal using a PRML signal processing system, the method comprising: a
pattern extraction step of extracting, from the binary signal, a plurality of state transition patterns
which have a possibility of causing a bit error; a differential metric computing step of computing
a differential metric, which is a difference of a first metric between an ideal signal of a most
likely first state transition sequence corresponding to the binary signal and the reproduction
signal, and a second metric between an ideal signal of a second most likely second state
transition sequence corresponding to the binary signal and the reproduction signal, based on the
binary signal for each state transition pattern extracted in the pattern extraction step; a first
integration step of integrating the differential metrics computed in the differential metric
computing step for each of the state transition patterns respectively; a first count step of counting
a number of times of integration processing in the first integration step for each of the state
transition patterns; a differential metric extraction step of extracting the differential metric not
greater than a predetermined signal processing threshold for each of the state transition patterns
respectively; a second integration step of integrating the differential metric not greater than the
signal processing threshold extracted in the differential metric extraction step for each of the
state transition patterns respectively: a second count step of counting a number of times of

integration processing in the second integration step for each of the state transition patterns: an
error rate computing step of computing, for each of the state transition patterns, a plurality of
error rates predicted based on the plurality of integration values that are integrated in the first
integration step, the plurality of count values that are counted in the first count step, the plurality
of integration values that are integrated in the second integration step, and the plurality of count
values that are counted in the second count step; a standard deviation computing step of
computing a standard deviation based on the total of the plurality of error rates that are computed
in the error rate computing step; and an evaluation step of evaluating a quality of the
reproduction signal, using the standard deviation computed in the standard deviation computing
[0293] According to the foregoing structure, a plurality of state transition patterns, which have
the possibility of causing a bit error, are extracted from the binary signal generated by
reproducing the information recording medium. And based on the binary signal, the differential
metric, which is a difference of a first metric between an ideal signal of a most likely first state
transition sequence corresponding to the binary signal and the reproduction signal, and a second
metric between an ideal signal of a second most likely second state transition sequence
corresponding to this binary signal and the reproduction signal, is computed for each of the
extracted state transition patterns respectively.
[0294] Then the calculated differential metric is integrated for each state transition pattern,
and a number of times of integration processing of the differential metrics is counted for each
state transition pattern respectively. The differential metrics, which are not greater than a
predetermined signal processing threshold, are extracted for each state transition pattern, the
extracted differential metrics, which are not greater than the signal processing threshold, are
integrated for each state transition pattern, and a number of integration processing of the
differential metric, which are not greater than the signal processing threshold, is counted for each
state transition pattern.
[0295] Then a plurality of error rates predicted based on the plurality of computed integration
values of the differential metrics, the plurality of count values of a number of times of integration
processing of the differential metrics, the plurality of integration values of the differential
metrics which are not greater than the predetermined signal processing threshold and the
plurality of count values of a number of times of integration processing of differential metrics
which are not greater than the predetermined signal processing threshold, are computed for each
state transition pattern. Further the standard deviation is computed based on the total of the
computed plurality of error rates, and the quality of the reproduction signal is evaluated using the
computed standard deviation.

[0296] Therefore when the mean value of the differential metrics does not match with the
code distance of the ideal signal depending on the recording state, an error of the standard
deviation, which is generated by a shift of the mean value of the differential metrics from the
code distance of the ideal signal, is corrected using the computed integration value of the
differential metrics and the count value of a number of times of integration processing of the
differential metrics, whereby correlation of the error rate and the signal index value is improved,
and the quality of the reproduction signal reproduced from the information recording medium
can be evaluated at high accuracy.
[0297] The reproduction signal evaluation unit according to another aspect of the present
invention is a reproduction signal evaluation unit for evaluating a quality of a reproduction signal
reproduced from an information recording medium based on a binary signal generated from the
reproduction signal using a PRML signal processing system, the unit comprising: a pattern
extraction section for extracting, from the binary signal, a specific state transition pattern which
has a possibility of causing a bit error; a differential metric computing section for computing a
differential metric, which is a difference of a first metric between an ideal signal of a most likely
first state transition sequence corresponding to the binary signal and the reproduction signal, and
a second metric between an ideal signal of a second most likely second state transition sequence
corresponding to the binary signal and the reproduction signal, based on the binary signal of the
state transition pattern extracted by the pattern extraction section; a first integration section for
integrating the differential metric computed by the differential metric computing section; a first
count section for counting a number of times of integration processing by the first integration
section; a differential metric extraction section for extracting the differential metric not greater
than a predetermined signal processing threshold; a second integration section for integrating the
differential metric not greater than the signal processing threshold extracted by the differential
metric extraction section; a second count section for counting a number of times of integration
processing by the second integration section; an error rate computing section for computing an
error rate predicted based on the integration value that is integrated by the first integration
section, the count value that is counted by the first count section, the integration value that is
integrated by the second integration section, and the count value that is counted by the second
count section; and a standard deviation computing section for computing a standard deviation
based on the error rate that is computed by the error rate computing section.
[029S] According to the foregoing structure, specific state transition patterns which have the
possibility of causing a bit error are extracted from the binary signals generated by reproducing
the information recording medium. Here the state transition pattern, which has a possibility of
causing a bit error, is a state transition pattern having merging paths which could take a plurality

of state transitions when a predetermined state at a certain time transits to a predetermined state
at another time, and is a state transition pattern of merging paths of which Euclidean distance
between an ideal signal of a most likely first state transition sequence and an ideal signal of a
second most likely second state transition sequence is relatively short. If there are a plurality of
state transition patterns which have the possibility of causing a bit error, a specific state
transition pattern is selectively extracted.
[0299] Targeting the binarv signal of the extracted SDecific state transition nattrn a
differential metric, which is a difference of a first metric between an ideal signal of a most likely
first state transition sequence corresponding to this binary signal and the reproduction signal, and
a second metric between an ideal signal of a second most likely second state transition sequence
corresponding to this binary signal and the reproduction signal, is computed.
[0300] Then the calculated differential metrics are integrated, and a number of times of
integration processing of the differential metrics are counted. Differential metrics which are not
greater than a predetermined signal processing threshold are extracted, and the extracted
differential metrics which are not greater than the signal processing threshold are integrated, and
a number of times of integration processing of the differential metrics which are not greater than
the signal processing threshold is counted.
[0301] Then an error rate, which is predicted based on the computed integration value of the
differential metrics, the count value of a number of times of integration processing of the
differential metrics, and the integration value of the differential metrics which are not greater
than the predetermined signal processing threshold and the count value of a number of times of
integration processing of the differential metrics which are not greater than the predetermined
signal processing threshold, is computed. Further the standard deviation is computed based on
the computed error rate, and the quality of the reproduction signal is evaluated using the
computed standard deviation.
[0302] Therefore when the mean value of the differential metrics does not match with the
code distance of the ideal signal, an error of the standard deviation, which is generated by a shift
of the mean value of the differential metrics from the code distance of the ideal signal, is
corrected using the computed integration value of the differential metrics, and the count value of
a number of times of integration processing of the differential metrics, whereby correlation of the
error rate and the signal index value is improved and the quality of the reproduction signal
reproduced from the information recording medium, can be evaluated at high accuracy.
[0303] In the foregoing reproduction signal evaluation unit, it is preferable that the signal
processing threshold is a square of a Euclidean distance between the ideal signal of the most
likely first state transition sequence and the ideal signal of the second most likely second state

transition sequence.
[0304] According to the foregoing structure, the signal processing threshold, corresponding to
the extraction target specific state transition pattern, can be accurately set so as to match with the
Euclidian distance between the ideal signal of the first state transition sequence and the ideal
signal of the second state transition sequence. This is particularly effective to evaluate signals
where a plurality of state transition patterns, which have the possibility of generating an error,
[0305] In the foregoing reproduction signal evaluation unit, it is preferable that the error rate
computing section computes a standard deviation of differential metrics not greater than a mean
value of differential metric outputs, using a linear expression of which arguments are the
integration value that is integrated by the first integration section, the count value that is counted
by the first count section, the integration value that is integrated by the second integration section,
and the count value that is counted by the second count section, and computes the error rate
based on the standard deviation.
[0306] According to the foregoing structure, the standard deviation of the differential metrics
not greater than the mean value of the differential metric outputs is computed using a linear
expression of which arguments are the computed integration value of the differential metrics, the
count value of a number of times of integration processing of the differential metrics, the
integration value of the differential metrics which are not greater than the predetermined signal
processing threshold and the count value of a number of times of integration processing of the
differential metrics which are not greater than the predetermined signal processing threshold
differential metric, and the error rate can be computed based on the standard deviation.
[0307] In the foregoing reproduction signal evaluation unit, it is preferable that the linear
expression is an approximate expression that is calculated using iteration based on the Newton's
method. According to the foregoing structure, the linear expression used for computing the error
rate can be given by an approximate expression that is computed and using iteration based on the
Newton's method.
[030S] In the foregoing reproduction signal evaluation unit, it is preferable that the error rate
computing section computes the error rate based on the mean value of the differential metrics
computed based on the integration value that is integrated by the first integration section and the
count value that is counted by the first count section, and a predetermined computing result
based on the integration value that is integrated by the second integration section and the count
value that is counted by the second count section.
[0309] According to the foregoing structure, the error rate can be computed based on the mean
value of the differential metrics computed based on the computed integration value of the

differential metric and the count value of a number of times of integration processing of
differential metrics, and a predetermined computing result based on the integration value of the
differential metrics which are not greater than the predetermined signal processing threshold and
the count value of a number of times of integration processing of the differential metrics which
are not greater than the predetermined signal processing threshold.
[0310] The reproduction signal evaluation unit according to another aspect of the present
invention is a reproduction signal evaluation unit for evaluating the Quality of a reproduction
signal reproduced from an information recording medium based on a binary signal generated
from the reproduction signal using a PRML signal processing system, the unit comprising: a
pattern extraction section for extracting, from the binary signal, a plurality of state transition
patterns which have a possibility of causing a bit error; a differential metric computing section
for computing a differential metric, which is a difference of a first metric between an ideal signal
of a most likely first state transition sequence corresponding to the binary signal and the
reproduction signal, and a second metric between an ideal signal of a second most likely second
state transition sequence corresponding to the binary signal and the reproduction signal, based on
the binary signal for each state transition pattern extracted by the pattern extraction section; a
first integration section for integrating the differential metric computed by the differential metric
computing section for each of the state transition patterns; a first count section for counting a
number of times of integration processing by the first integration section for each of the state
transition patterns; a differential metric extraction section for extracting the differential metric
not greater than a predetermined signal processing threshold for each of the state transition
patterns; a second integration section for integrating the differential metric not greater than the
signal processing threshold extracted by the differential metric extraction section for each of the
state transition patterns; a second count section for counting a number of times of integration
processing by the second integration section for each of the state transition patterns; an error rate
computing section for computing, for each of the state transition patterns, a plurality of error
rates predicted based on the plurality of integration values that are integrated by the first
integration section, the plurality of count values that are counted by the first count section, the
plurality of integration values that are integrated by the second integration section, and the
plurality of count values that are counted by the second count section; and a standard deviation
computing section for computing a standard deviation based on the total of the plurality of error
rates that are computed by the error rate computing section.
[0311] According to the foregoing structure, a plurality of state transition patterns, which have
the possibility of causing a bit error, are extracted from the binary signal generated by
reproducing the information recording medium. And based on the binary signal, the differential

metric, which is a difference of a first metric between an ideal signal of a most likely first state
transition sequence corresponding to the binary signal and the reproduction signal, and a second
metric between an ideal signal of a second most likely second state transition sequence
corresponding to this binary signal and the reproduction signal, is computed for each of the
extracted state transition patterns respectively.
[0312] Then the calculated differential metrics are integrated for each state transition pattern,
and a number of times of integration processing of the differential metric is counted for each
state transition pattern respectively. Differential metrics, which are not greater than a
predetermined signal processing threshold, are extracted for each state transition pattern, the
extracted differential metrics, which are not greater than the signal processing threshold, are
integrated for each state transition pattern, and a number of integration processing of the
differential metric, which are not greater than the signal processing threshold, is counted for each
state transition pattern.
[0313] Then a plurality of error rates predicted based on the plurality of computed integration
values of the differential metrics, the plurality of count values of a number of times of integration
processing of the differential metrics, the plurality of integration values of the differential
metrics which are not greater than the predetermined signal processing threshold and the
plurality of count values of a number of times of integration processing of the differential metrics
which are not greater than the predetermined signal processing threshold, are computed for each
state transition pattern. Further the standard deviation is computed based on the total of the
computed plurality of error rates, and the quality of the reproduction signal is evaluated using the
computed standard deviation.
[0314] Therefore when the mean value of the differential metrics does not match with the
code distance of the ideal signal depending on the recording stale, an error of the standard
deviation, which is generated by a shift of the mean value of the differential metrics from the
code distance of the ideal signal, is corrected using the computed integration value of the
differential metrics and the count value of a number of times of integration processing of the
differential metrics, whereby correlation of the error rate and the signal index value is improved,
and the quality of the reproduction signal reproduced from the information recording medium
can be evaluated at high accuracy.
[0315] An optical disk device according to another aspect of the present invention, comprises:
a reproduction section for generating a binary signal from a reproduction signal reproduced from
an optical disk, which is an information recording medium, using a PRML signal processing
system: and the preproduction signal evaluation unit according to one of the above descriptions.
According to this structure, the foregoing reproduction signal evaluation unit can be applied to

the optical disk device.
[0316] Specific embodiments or examples for the detailed description of the invention are
merely to clarify the technical content of the present invention, and the present invention should
not be interpreted within these limited examples, but can be modified in various ways within the
spirit of the present invention and scope of the Claims described herein below.
INDUSTRIAL APPLICABILITY
[0317] The present invention is particularly useful in technical fields in which signal
processing is performed using the maximum likelihood decoding method.

CLAIMS
1. A reproduction signal evaluation method for evaluating a quality of a reproduction
signal reproduced from an information recording medium based on a binary signal generated
from the reproduction signal using a PRML signal processing system,
the method comprising:
a pattern extraction step of extracting, from the binary signal, a specific state transition
pattern which has a possibility of causing a bit error;
a differential metric computing step of computing a differential metric, which is a
difference of a first metric between an ideal signal of a most likely first state transition sequence
corresponding to the binary signal and the reproduction signal, and a second metric between an
ideal signal of a second most likely second state transition sequence corresponding to the binary
signal and the reproduction signal, based on the binary signal of the state transition pattern
extracted in the pattern extraction step;
a first integration step of integrating the differential metric computed in the differential
metric computing step;
a first count step of counting a number of times of integration processing in the first
integration step;
a differential metric extraction step of extracting the differential metric not greater than
a predetermined signal processing threshold;
a second integration step of integrating the differential metric not greater than the
signal processing threshold extracted in the differential metric extraction step;
a second count step of counting a number of times of integration processing in the
second integration step;
an error rate computing step of computing an error rate predicted based on an
integration value that is integrated in the first integration step, a count value that is counted in the
first count step, an integration value that is integrated in the second integration step, and a count
value that is counted in the second count step;
a standard deviation computing step of computing a standard deviation based on the
error rate that is computed in the error rate computing step; and
an evaluation step of evaluating a quality of the reproduction signal using the standard
deviation computed in the standard deviation computing step.
2. The reproduction signal evaluation method according to Claim 1. wherein the signal
processing threshold is a square of a Euclidian distance between the ideal signal of the most

likely first state transition sequence and the ideal signal of the second most likely second state
transition sequence.
error rate computing step computes a standard deviation of differential metrics not greater than a
mean value of differential metric outputs, using a linear expression of which arguments are the
integration value that is integrated in the first integration steD. the count value that is counted in

the first count step, the integration value that is integrated in the second integration step, and the
count value that is counted in the second count step, and computes the error rate based on the
standard deviation.
4. The reproduction signal evaluation method according to Claim 3, wherein the linear
expression is an approximate expression that is calculated using iteration based on the Newton's
method.
5. The reproduction signal evaluation method according to any one of Claims 1 through 4,
wherein the error rate computing step computes the error rate based on the mean value of the
differential metrics computed based on the integration value that is integrated in the first
integration step and the count value that is counted in the first count step, and a predetermined
computing result based on the integration value that is integrated in the second integration step
and the count value that is counted in the second count step.
6. A reproduction signal evaluation method for evaluating a quality of a reproduction
signal reproduced from an information recording medium based on a binary signal generated
from the reproduction signal using a PRML signal processing system.
the method comprising:
a pattern extraction step of extracting, from the binary signal, a plurality of state
transition patterns which have a possibility of causing a bit error;
a differential metric computing step of computing a differential metric, which is a
difference of a first metric between an ideal signal of a most likely first state transition sequence
corresponding to the binary signal and the reproduction signal, and a second metric between an
ideal signal of a second most likely second state transition sequence corresponding to the binary
signal and the reproduction signal, based on the binary signal for each state transition pattern
extracted in the pattern extraction step:
a first integration step of integrating the differential metrics computed in the

differential metric computing step for each of the state transition patterns respectively;
a first count step of counting a number of times of integration processing in the first
integration step for each of the state transition patterns;
a differential metric extraction step of extraction the differential metrics not greater
than a predetermined signal processing threshold for each of the state transition patterns
respectively;
a second integration step of integrating the differential metrics not greater than the
signal processing threshold extracted in the differential metric extraction step for each of the
state transition patterns respectively;
a second count step of counting a number of times of integration processing in the
second integration step for each of the state transition patterns;
an error rate computing step of computing, for each of the state transition patterns, a
plurality of error rates predicted based on the plurality of integration values that are integrated in
the first integration step, the plurality of count values that are counted in the first count step, the
plurality of integration values that are integrated in the second integration step, and the plurality
of count values that are counted in the second count step;
a standard deviation computing step of computing a standard deviation based on the
total of the plurality of error rates that are computed in the error rate computing step; and
an evaluation step of evaluating a quality of the reproduction signal, using the standard
deviation computed in the standard deviation computing step.
7. A reproduction signal evaluation unit for evaluating a quality of a reproduction signal
reproduced from an information recording medium based on a binary signal generated from the
reproduction signal using a PRML signal processing system,
the unit comprising:
a pattern extraction section for extracting, from the binary signal, a specific state
transition pattern which has a possibility of causing a bit error;
a differential metric computing section for computing a differential metric, which is a
difference of a first metric between an ideal signal of a most likely first state transition sequence
corresponding to the binary signal and the reproduction signal, and a second metric between an
ideal signal of a second most likely second state transition sequence corresponding to the binary
signal and the reproduction signal, based on the binary signal of the state transition pattern
extracted by the pattern extraction section;
a first integration section for integrating the differential metric computed by the
differential metric computing section;

a first count section for counting a number of times of integration processing by the
first integration section;
a differential metric extraction section for extracting the differential metric not greater
than a predetermined signal processing threshold;
a second integration section for integrating the differential metric not greater than the
signal processing threshold extracted by the differential metric extraction section;
a second count section for counting a number of times of integration processing by the
second integration section;
an error rate computing section for computing an error rate predicted based on the
integration value that is integrated by the first integration section, the count value that is counted
by the first count section, the integration value that is integrated by the second integration section,
and the count value that is counted by the second count section; and
a standard deviation computing section for computing a standard deviation based on
the error rate that is computed by the error rate computing section.
8. The reproduction signal evaluation unit according to Claim 7, wherein the signal
processing threshold is a square of a Euclidean distance between the ideal signal of the most
likely first state transition sequence and the ideal signal of the second most likely second state
transition sequence.
9. The reproduction signal evaluation unit according to Claim 7 or 8, wherein the error
rate computing section computes a standard deviation of differential metric not greater than a
mean value of the differential metric outputs, using a linear expression of which arguments are
the integration value that is integrated by the first integration section, the count value that is
counted by the first count section, the integration value that is integrated by the second
integration section, and the count value that is counted bv the second count section, and
computes the error rate based on the standard deviation.
10. The reproduction signal evaluation unit according to Claim 9. wherein the linear
expression is an approximate expression that is calculated using iteration based on the Newton's
method.
11. The reproduction signal evaluation unit according to any one of Claims 7 through 10,
wherein the error rate computing section computes the error rate based on the mean value of the
differential metrics computed based on the integration value that is integrated by the first

integration section and the count value that is counted by the first count section, and a
predetermined computing result based on the integration value that is integrated by the second
integration section and the count value that is counted by the second count section.
12. A reproduction signal evaluation unit for evaluating a quality of a reproduction signal
reproduced from an information recording medium based on a binary signal generated from the
reproduction signal using a PRML signal processing system,
the unit comprising:
a pattern extraction section for extracting, from the binary signal, a plurality of state
transition patterns which have a possibility of causing a bit error;
a differential metric computing section for computing a differential metric, which is a
difference of a first metric between an ideal signal of a most likely first state transition sequence
corresponding to the binary signal and the reproduction signal, and a second metric between an
ideal signal of a second most likely second state transition sequence corresponding to the binary
signal and the reproduction signal, based on the binary signal for each state transition pattern
extracted by the pattern extraction section;
a first integration section for integrating the differential metric computed by the
differential metric computing section for each of the state transition patterns;
a first count section for counting a number of times of integration processing by the
first integration section for each of the state transition patterns;
a differential metric extraction section for extracting the differential metric not greater
than a predetermined signal processing threshold for each of the state transition patterns;
a second integration section for integrating the differential metric not greater than the
signal processing threshold extracted by the differential metric extraction section for each of the
state transition patterns;
a second count section for counting a number of times of integration processing by the
second integration section for each of the state transition patterns;
an error rate computing section for computing, for each of the state transition patterns,
a plurality of error rates predicted based on the plurality of integration values that are integrated
by the first integration section, the plurality of count values that are counted by the first count
section, the plurality of integration values that are integrated by the second integration section,
and the plurality of count values that are counted by the second count section: and
a standard deviation computing section for computing a standard deviation based on
the total of the plurality of error rates that are computed by the error rate computing section.

13. An optical disk device, comprising:
a reproduction section for generating a binary signal from a reproduction signal
reproduced from an optical disk, which is an information recording medium, using a PRML
signal processing system; and
the preproduction signal evaluation unit according to any one of Claims 7 through 12.

A reproduction signal evaluation unit has: a pattern detection section (101) for
extracting, from a binary signal, a specific state transition pattern which has a possibility of
causing a bit error; a differential metric computing section (102) for computing a differential
metric based on the binary signal of the extracted state transition pattern; an error computing
section (116) for computing an error rate predicted based on an integration value that is
integrated by an integration section (125), a count value that is counted by a pattern count section
(124), an integration value that is integrated by another integration section (105), and a count
value that is counted by another pattern count section (104), and a standard deviation computing
section (120) for computing a standard deviation based on the computed error rate.