Dependent Quantization

Description

The present application is concerned with media signal coding such as video coding and, especially, lossy codecs using quantization in encoding the media signal such as, for instance, for quantizing a prediction residual as done, for instance, in HEVC.

In setting a quantization parameter, the encoder has to make a compromise. Rendering the quantization coarse reduces the bitrate, but increases the quantization distortion, and rendering the quantization finer decreases the distortion, but increases the bitrate. It would be favorable to have a concept at hand which increases the coding efficiency for a given domain of available quantization levels.

It is the object of the present invention to provide a concept for coding a media signal using quantization which achieves such an increase in coding efficiency at a given set of available quantization levels.

This object is achieved by the subject-matter of the independent claims of the present application.

The present application is based on the finding that the coding of a media signal may be rendered more efficient by describing the media signal using a sequence of samples and sequentially encoding this sequence by selecting, for a current sample, a set, such as a countable set, of quantization levels, also called reconstruction levels, out of a plurality of quantization level sets depending on indices encoded into the data stream for previous samples of the sequence of samples, quantizing the current sample onto one level of the set of quantization levels, and encoding a quantization index to the one level for the current sample into the data stream. In other words, scalar quantization of the individual samples of the sequence of samples is used, but it is rendered dependent on quantization indices encoded into the data stream for previous samples of the sequence of samples. By this measure, it is possible to“construe” a grid of quantization points in the multi dimensional space across which all possible settings of the sequence of samples are spread, onto which quantizations of the samples are quantized according to the sequence

of quantization indices coded into the data stream. This grid, in turn, reduces, statistically, a quantization mean error.

In accordance with an embodiment, the media signal is a two-dimensional signal, such as a picture, and a sequence of samples is obtained by use of some scanning pattern which turns the two-dimensional spatial arrangement of the samples into a one-dimensional sequence along which, then, the construction of the aforementioned quantization point grid takes place.

In accordance with an embodiment, the sequence of samples which describe the media signal represents a transform block of a picture or a portion thereof, i.e. , it is obtained via transforming a transform block of the picture, i.e. a picture block or block of spatial samples such as residual samples of a predictor, into a transform coefficient block wherein predetermined transform coefficients of the transform coefficient block, scanned along a predetermined coefficient scan, form the sequence of samples. The transformation may be a linear transformation or any other transformation, and for reconstruction purposes, the inverse transformation or some other reverse transformation approximating the inverse may be used. Additionally or alternatively, the sequence of samples may represent a prediction residual.

In accordance with an embodiment, the selection of the set of quantization levels for the current sample depends on a least significant bit portion such as merely a parity of the quantization indices encoded into the data stream for previous samples of the sequence of samples. In particular, in accordance with an embodiment, the number of quantization level sets of the plurality of quantization level sets from which the selection takes place is two. Additionally or alternatively, the selection out of the plurality of quantization level sets may depend on a parity of the previous quantization indices. In particular, a fixed construction rule may be used for the selection for the samples which selects one out of the plurality of quantization level sets depending on the quantization indices coded for a predetermined fixed number of immediately preceding samples of the sequence of samples. In accordance with an example, a state transition process or state machine is used to this end which comprises a set of states and transitions from one state for one sample to a state for the next sample in the sequence of samples, the state transitioning from one state to the next depending on the quantization index for the one sample such as the parity thereof. Each state uniquely determines the set of quantization levels to be used for the sample the respective state is associated with. A predetermined state setting

may be applied at encoder and decoder for an initial state using which the state transitioning is commenced. For instance, this predetermined number may be two and the cardinality of the plurality of quantization level sets may also be two. As just-mentioned, the dependency may merely relate to the parity of the number of quantization indices. This results into an easy implementation at improved coding efficiency.

Favorably, and in accordance with an embodiment of the present application, the plurality of quantization level sets is parameterized by way of a predetermined quantization step size and information on the predetermined quantization step size is signaled in the data stream. In case of the sequence of samples representing transform coefficients of a transform block, for each transform coefficient (sample) an own quantization step size for parameterizing the plurality of quantization level sets may be determined. For instance, the quantization step sizes for the transform coefficients of a transform block may be related to one signaled quantization step size signaled in the data stream in a predetermined manner. For instance, one quantization step size may be signaled in the data stream for the whole transform block and be individually scaled for each transform coefficient according to a scale factor which is set by default or also coded in the data stream.

Accordingly, advantageously, the dependent scalar quantization used herein allows for a combination of this dependent scalar quantization with a concept of using a weighting matrix for weighting the scaling factor across transform coefficients of a transform block.

The selected quantization level set may be taken to account in entropy coding an absolute value of the quantization index. Additionally, context dependency may include a dependency on quantization levels for preceding samples of the sequence of samples such as in a local neighborhood of the current sample. Advantageous aspects are the subject of dependent claims. Preferred embodiments of the present application are described below with respect to the figures among which:

Fig. 1 shows a block diagram of an exemplary video encoder as an example for a picture encoder which may be embodied to operate in accordance with any of the embodiments described below.

Fig. 2 shows a block diagram of (a) a transform encoder; and (b) a transform decoder to illustrate a basic approach of block-based transform coding;

Fig. 3 shows a histogram of a distribution illustrating a uniform reconstruction quantizer.

Fig. 4 shows a schematic diagram of (a) a transform block subdivided into subblocks and (b) a subblock in order to illustrate an example for scanning of transform coefficient levels, here exemplarily one used in H.265 | MPEG- H HEVC; in particular, (a) shows a partitioning of a 16x16 transform block into 4x4 subblocks and the coding order of subblocks; (b) shows the coding order of transform coefficient levels inside a 4x4 subblock.

Fig. 5 shows a schematic diagram of a multi-dimensional output space spanned by one axis per transform coefficient, and the location of admissible reconstruction vectors for the simple case of two transform coefficients: (a) Independent scalar quantization; (b) an example for dependent scalar quantization.

Fig. 6a shows a block diagram of a transform decoder using dependent scalar quantization, thereby forming an embodiment of a media decoder according to the present application. Modifications relative to conventional transform coding (with independent scalar quantizers) are derivable by comparison to Fig. 2b.

Fig. 6b shows a block diagram of a transform encoder using dependent scalar quantization, thereby forming an embodiment of a media encoder according to the present application. Modifications relative to conventional transform coding (with independent scalar quantizers) are derivable by comparison to Fig. 2a.

Fig. 7a shows a schematic diagram of a concept for quantization performed within an encoder for encoding transform coefficients according to an embodiment such as by quantization stage of Fig. 6b.

Fig. 7b shows a schematic diagram of a concept for dequantization performed within a decoder for decoding transform coefficients according to an embodiment such as by dequantization stage of Fig. 6a.

Fig. 8a,b,c schematic diagrams of collections of available quantization sets between which switching is done according to previous levels; in particular, examples for dependent quantization with two sets of reconstruction levels that are completely determined by a single quantization steps size D is shown. The two available sets of reconstruction levels are highlighted with different colors (blue for set 0 and red for set 1 ). Examples for quantization indexes that indicate a reconstruction level inside a set are given by the numbers below the circles. The hollow and filled circles indicate two different subsets inside the sets of reconstruction levels; the subsets can be used for determining the set of reconstruction levels for the next transform coefficient in reconstruction order. The figures show three configurations with two sets of reconstruction levels: (a) The two sets are disjoint and symmetric with respect to zero; (b) Both sets include the reconstruction level equal to zero, but are otherwise disjoint; the sets are non-symmetric around zero; (c) Both sets include the reconstruction level equal to zero, but are otherwise disjoint; both sets are symmetric around zero.

Fig. 9a shows a pseudo-code illustrating an example for the reconstruction process for transform coefficients k represents an index that specifies the reconstruction order of the current transform coefficient, the quantization index for the current transform coefficient is denoted by level[k], the quantization step size A k that applies to the current transform coefficient is denoted by quant_step_size[k], and trec[k] represents the value of the reconstructed transform coefficient tk' . The variable setld[k] specifies the set of reconstruction levels that applies to the current transform coefficient. It is determined based on the preceding transform coefficients in reconstruction order; the possible values of setld[k] are 0 and 1. The variable n specifies the integer factor of the quantization step size; it is given by the chosen set of reconstruction levels (i.e., the value of setld[kj) and the transmitted quantization index level[k].

Fig. 9b shows a pseudo-code illustrating an alternative implementation of the pseudo-code in Fig. 9a. The main change is that the multiplication with the quantization step is represented using an integer implementation using a scale and a shift parameter. Typically, the shift parameter (represented by shift) is constant for a transform block and only the scale parameter (given by scale[k]) may depend on the location of the transform coefficient. The variable add represents a rounding offset, it is typically set equal to add = (1 «(shift-1 )). With Ak being the nominal quantization step for the transform coefficient, the parameters shift and scalejYj are chosen in a way that we have

Fig. 10a shows a schematic diagram of an example for a splitting of the sets of reconstruction levels into two subsets. The two shown quantization sets are the quantization sets of the example of Fig. 8c. The two subsets of the quantization set 0 are labeled using“A” and“B”, and the two subsets of quantization set 1 are labeled using“C” and“D”.

Fig. 10b shows a table as an example for the determination of the quantization set

(set of available reconstruction levels) that is used for the next transform coefficient based on the subsets that are associated with the two last quantization indexes. The subsets are shown in the left table column; they are uniquely determined by the used quantization set (for the two last quantization indexes) and the so-called path (which may be determined by the parity of the quantization index). The quantization set and, in parenthesis, the path for the subsets are listed in the second column form the left. The third column specifies the associated quantization set. In the last column, the value of a so-called state variable is shown, which can be used for simplifying the process for determining the quantization sets.

Fig. 10c shows a state transition table as a further example as to how to switch between the available quantization sets, here for a configuration with 4 states.

Fig. 1 1 shows a pseudo-code illustrating an example for the reconstruction process of transform coefficients for a transform block. The array level represents the transmitted transform coefficient levels (quantization indexes) for the transform block and the array tree represent the corresponding reconstructed transform coefficients. The 2d table state_trans_table specifies the state transition table and the table setld specifies the quantization set that is associated with the states.

Fig. 12 shows examples for the state transition table state _trans_table and the table setld, which specifies the quantization set associated with the states. The table given in C-style syntax represents the tables specified in the table of Fig. 10c.

Fig. 13 shows a pseudo-code illustrating an alternative reconstruction process for transform coefficient levels, in which quantization index equal to 0 are excluded from the state transition and dependent scalar quantization.

Fig. 14 shows a schematic diagram illustrating a state transition in dependent scalar quantization as trellis structure. The horizontal exists represents different transform coefficients in reconstruction order. The vertical axis represents the different possible states in the dependent quantization and reconstruction process. The shown connections specify the available paths between the states for different transform coefficients.

Fig. 15 shows an example of a basic trellis cell.

Fig. 16 shows a schematic diagram of a trellis example for dependent scalar quantization of 8 transform coefficients. The first state (left side) represents an initial state, which is set equal to 0 in this example.

Fig. 17a shows a schematic diagram of a concept for entropy decoding quantization levels performed within an encoder for encoding transform coefficients according to an embodiment such as by the entropy decoder in Fig. 6b.

Fig. 17b shows a schematic diagram of a concept for entropy encoding quantization levels performed within an encoder for encoding transform coefficients according to an embodiment such as by the entropy encoder in Fig. 6a.

Fig. 18 shows a table with examples for the binarization of the absolute values of quantization indexes. From left to right: (a) Unary binarization; (b) Exp-Golomb binarization; (c) Concatenated binarization consisting of a unary prefix part (first two bins marked blue) and an Exp-Golumb suffix part; (d) Concatenated binarization consisting of a unary prefix part (first two bins marked blue), a bin indicating the path/parity (red) and an Exp-Golumb suffix part (individual code for both paths/partities).

Fig. 19 shows a schematic diagram of a transform block for illustration of concepts for the entropy coding of transform coefficient levels: (a) Signaling of the position of the first non-zero quantization index in coding order (black sample). In addition to the position of the first non-zero transform coefficients, only bins for the blue-marked coefficients are transmitted, the white-marked coefficients are inferred to be equal to 0. (b) Example for a template that is used for selecting probability models for one or more bins.

Fig. 20 shows a schematic diagram of an example for a trellis structures that can be exploited for determining sequences (or blocks) of quantization indexes that minimize a cost measures (such as an Lagrangian cost measure D + A R). The trellis structure represents a further example of dependent quantization with 4 states (see Fig. 16). The trellis is shown for 8 transform coefficients (or quantization indexes). The first state (at the very left) represents an initial state, which is assumed to be equal to 0.

Fig. 21 shows a block diagram of a decoder which may be implemented to operate in accordance with an embodiment such as the one depicted in Fig. 7b, and fits to the encoder example of Fig. 1.

Fig. 22 shows a schematic diagram of an example for subdivisions of a picture with respect to prediction and residual coding and the relationship thereamong.

The following description describes a concept for media signal coding using dependent scalar quantization. For ease of understanding, however, concrete examples set forth below relate to transform coding of transform coefficients using dependent scalar quantization. As mentioned below, the embodiments of the present application are, however, not restricted to transform coding.

In accordance with the embodiments described next, transform coding involves a transform of a set of samples, a dependent scalar quantization of the resulting transform coefficients, and an entropy coding of the obtained quantization indices. At the decoder side, the set of reconstructed samples is obtained by entropy decoding of the quantization indices, a dependent reconstruction of transform coefficients, and an inverse transform. In contrast to conventional transform coding which consists of a transform, independent scalar quantization and entropy coding, the set of admissible reconstruction levels for a transform coefficient depends on the transmitted quantization indices which are also referred to as transform coefficient levels that precede the current transform coefficient in reconstruction order. Additionally, entropy coding of quantization indices, i.e., transform coefficient levels, that specify the reconstruction levels used in dependent scalar quantization are described. Even further, an adaptive selection between conventional transform coding and transform coding with dependent scalar quantization and concepts for adapting the quantization step size used for dependent scalar quantization are described. The following description mainly targets on lossy coding of blocks of prediction error samples in image and video codecs, but it is noted that the embodiments described below may also be applied to other areas of lossy coding such as, for instance, audio coding or the like. That is, the embodiments described below are not restricted to sets of samples that form rectangular blocks and are not restricted to sets of samples that represent prediction error samples, i.e., differences between an original and a prediction signal. Rather, the embodiments described below may readily be transferred to other scenarios such as audio signal coding, coding without prediction or coding in spatial domain rather than transform domain.

All state-of-the-art video codecs, such as the international video coding standards H.264 | MPEG-4 AVC [1] and H.265 | MPEG-H HEVC [2] follow the basic approach of hybrid video coding. The video pictures are partitioned into blocks, the samples of a block are predicted using intra-picture prediction or inter-prediction, and the samples of the resulting prediction error signal (difference between the original samples and the samples of the prediction signal) are coded using transform coding.

Fig. 1 shows a simplified block diagram of a typical modern video encoder. The video pictures of a video sequence are coded in a certain order, which is referred to as coding order. The coding order of pictures can differ from the capture and display order. For the actual coding, each video picture is partitioned into blocks. A block comprises the samples of a rectangular area of a particular color component. The entity of the blocks of all color components that correspond to the same rectangular area is often referred to as unit. Depending on the purpose of the block partitioning, in H.265 | MPEG-H HEVC, it is differentiated between coding tree blocks (CTBs), coding blocks (CBs), prediction blocks (PBs), and transform blocks (TBs). The associated units are referred to as coding tree units (CTUs), coding units (CUs), prediction units (PUs), and transform units (TUs).

Typically, a video picture is initially partitioned into fixed sized units (i.e., aligned fixed sized blocks for all color components). In H.265 | MPEG-H HEVC, these fixed sized units are referred to as coding tree units (CTUs). Each CTU can be further split into multiple coding units (CUs). A coding unit is the entity for which a coding mode (for example, intra-or inter-picture coding) is selected. In H.265 | MPEG-H HEVC, the decomposition of a CTU into one or multiple CUs is specified by a quadtree (QT) syntax and transmitted as part of the bitstream. The CUs of a CTU are processed in the so-called z-scan order. That means, the four blocks that result from a split are processed in raster-scan order; and if any of the blocks is further partitioned, the corresponding four blocks (including the included smaller blocks) are processed before the next block of the higher splitting level is processed.

If a CU is coded in an intra coding mode, an intra prediction mode for the luma signal and, if the video signal includes chroma components, another intra prediction mode for the chroma signals is transmitted. In ITU-T H.265 | MPEG-H HEVC, if the CU size is equal to the minimum CU size (as signaled in the sequence parameter set), the luma block can also be split into four equally sized blocks, in which case, for each of these blocks, a separate luma intra prediction mode is transmitted. The actual intra prediction and coding is done on the basis of transform blocks. For each transform block of an intra-picture coded CU, a prediction signal is derived using already reconstructed samples of the same color component. The algorithm that is used for generating the prediction signal for the transform block is determined by the transmitted intra prediction mode.

CUs that are coded in inter-picture coding mode can be further split into multiple prediction units (PUs). A prediction unit is the entity of a luma and, for color video, two associated chroma blocks (covering the same picture area), for which a single set of prediction parameters is used. A CU can be coded as a single prediction unit, or it can be split into two non-square (symmetric and asymmetric splittings are supported) or four square prediction units. For each PU, an individual set of motion parameters is

transmitted. Each set of motion parameters includes the number of motion hypotheses

(one or two in H.265 | MPEG-H HEVC) and, for each motion hypothesis, the reference picture (indicated via a reference picture index into a list of reference pictures) and the associated motion vector. In addition, H.265 | MPEG-H HEVC provides a so-called merged mode, in which the motion parameters are not explicitly transmitted, but derived based on motion parameters of spatial or temporal neighboring blocks. If a CU or PU is coded in merge mode, only an index into a list of motion parameter candidates (this list is derived using motion data of spatial and temporal neighboring blocks) is transmitted. The index completely determines the set of motion parameters used. The prediction signal for inter-coded PUs is formed by motion-compensated prediction. For each motion hypothesis (specified by a reference picture and a motion vector), a prediction signal is formed by a displaced block in the specified reference picture, where the displacement relative to the current PU is specified by the motion vector. The displacement is typically specified with sub-sample accuracy (in H.265 j MPEG-H HEVC, the motion vectors have a precision of a quarter luma sample). For non-integer motion vectors, the prediction signal is generated by interpolating the reconstructed reference picture (typically, using separable FIR filters). The final prediction signal of PUs with multi-hypothesis prediction is formed by a weighted sum of the prediction signals for the individual motion hypothesis. Typically, the same set of motion parameters is used for luma and chroma blocks of a PU. Even though state-of-the-art video coding standards use translational displacement vectors for specifying the motion of a current area (block of samples) relative to a reference picture, it is also possible to employ higher-order motion models (for example, the affine motion model). In that case, additional motion parameters have to be transmitted for a motion hypothesis.

For both intra-picture and inter-picture coded CUs, the prediction error signal (also called residual signal) is typically transmitted via transform coding. In H.265 | MPEG-H HEVC, the block of luma residual samples of a CU as well as the blocks of chroma residual samples (if present) are partitioned into transform blocks (TBs). The partitioning of a CU into transform block is indicated by a quadtree syntax, which is also referred to as residual quadtree (RQT). The resulting transform blocks are coded using transform coding: A 2d transform is applied to the block of residual samples, the resulting transform coefficients are quantized using independent scalar quantization, and the resulting transform coefficient levels (quantization indexes) are entropy coded. In P and B slices, at the beginning of the CU syntax, a skip flag is transmitted. If this flag is equal to 1 , it indicates that the corresponding CU consists of a single prediction unit coded in merge mode (i.e.,

merge_flag is inferred to be equal to 1 ) and that all transform coefficients are equal to zero (i.e., the reconstruction signal is equal to the prediction signal). In that case, only the mergejdx is transmitted in addition to the skip flag. If skipjlag is equal to 0, the prediction mode (inter or intra) is signaled, followed by the syntax features described above.

Since already coded pictures can be used for motion-compensated prediction of blocks in following pictures, the pictures have to be fully reconstructed in the encoder. The reconstructed prediction error signal for a block (obtained by reconstructing the transform coefficients given the quantization indexes and an inverse transform) is added to the corresponding prediction signal and the result is written to a buffer for the current picture. After all blocks of a picture are reconstructed, one or more in-loop filters can be applied (for example, a deblocking filter and a sample adaptive offset filter). The final reconstructed picture is then stored in a decoded picture buffer.

In the following, a new concept for transform coding of prediction error signals is described. The concept is applicable for both intra-picture and inter-picture coded blocks. It is also applicable to transform coding of non-rectangular sample regions. In contrast to conventional transform coding, the transform coefficients are not independently quantized. Instead, the set of available reconstruction levels for a particular transform coefficient depends on the chosen quantization indexes for other transform coefficients. Further, modifications for the entropy coding of quantization indexes, which increase the coding efficiency when combined with dependent scalar quantization are described.

All major video coding standards, including the state-of-the-art standard H.265 | MPEG-H HEVC, discussed above utilize the concept of transform coding for coding blocks of prediction error samples. The prediction error samples of a block represent the differences between the samples of the original signal and the samples of a prediction signal for the block. The prediction signal is either obtained by intra-picture prediction (in which case the samples of the prediction signal for a current block are derived based on already reconstructed samples of neighboring blocks inside the same picture) or by inter-picture prediction (in which case the samples of the prediction signal are derived based on samples of already reconstructed pictures). The samples of the original prediction error signal are obtained by subtracting the values of the samples of the prediction signal from the samples values of the original signal for the current block.

Transform coding of sample blocks may consist of a linear transformation, scalar quantization, and entropy coding of the quantization indexes. At the encoder side (see Fig. 2a), an NxM block of original samples is transformed using a linear analysis transform A. The result is an N*M block of transform coefficients. The transform coefficients tk represent the original prediction error samples in a different signal space (or different coordinate system). The NxM transform coefficients are quantized using NxM independent scalar quantizers. Each transform coefficient tk is mapped to a quantization index qk, which is also referred to as transform coefficient level. The obtained quantization indexes qk are entropy coded and written to the bitstream.

At the decoder side (see Fig. 2b), the transform coefficient levels qk are decoded from the received bitstream. Each transform coefficient level qk is mapped to a reconstructed transform coefficient t’k. The NxM block of reconstructed samples is obtained by transforming the block of reconstructed transform coefficients using a linear synthesis transform B.

Even though video coding standards only specify the synthesis transform B, it is common practice that the inverse of the synthesis transform B is used as analysis transform A in an encoder, i.e. , A = B- 1. Moreover, the transforms used in practical video coding systems represent orthogonal transforms (B- 1 = BT) or nearly orthogonal transforms. For orthogonal transforms, the mean squared error (MSE) distortion in the signal space is equal to the MSE distortion in the transform domain. The orthogonality has the important advantage that the MSE distortion between an original and reconstructed sample block can be minimized using independent scalar quantizers. Even if the actual quantization process used in an encoder takes dependencies between transform coefficient levels (introduced by the entropy coding, see below) into account, the usage of orthogonal transforms significantly simplifies the quantization algorithm.

For typical prediction error signals, the transform has the effect that the signal energy is concentrated in a few transform coefficients. In comparison to the original prediction error samples, the statistical dependencies between the resulting transform coefficients are reduced.

In state-of-the-art video coding standards, a separable discrete cosine transform (type II) or an integer approximation thereof is used. The transform can, however, be easily replaced without modifying other aspects of the transform coding system. Examples for

improvements that have been suggested in the literature or in standardization documents include:

• Usage of discrete sine transform (DST) for intra-picture predicted blocks (possibly depending on the intra prediction mode and/or the block size). Note that H.265 |

MPEG-H HEVC already includes a DST for intra-picture predicted 4*4 transform blocks.

• Switched transforms: The encoder selects the actually used transform among a set of pre-defined transforms. The set of available transforms is known by both the encoder and the decoder, so that it can be efficiently signaled using an index into a list of available transforms. The set of available transforms and their ordering in a list can depend on other coding parameters for the block, such as the chosen intra prediction mode. In a special case, the used transform is completely determined by coding parameters such as the intra prediction mode and/or the block shape, so that no syntax element for specifying the transform needs to be transmitted.

• Non-separable transforms: The transforms used in encoder and decoder represent non-separable transforms. Note that the concept of switched transforms may include one or more non-separable transforms. Due to complexity reasons, the usage of non-separable transforms can be restricted to certain block sizes.

• Multi-level transforms: The actual transform is composed of two or more transform stages. The first transform stage could consist of a computationally low-complex separable transform. And in the second stage a subset of the resulting transform coefficients is further transformed using a non-separable transform. It comparison to a non-separable transform for the entire transform block, the two-stage approach has the advantage that the more complex non-separable transform is applied to a smaller number of samples. The concept of multi-level transforms can be efficiently combined with the concept of switched transforms.

The transform coefficients are quantized using scalar quantizers. As a result of the quantization, the set of admissible values for the transform coefficients is reduced. In other words, the transform coefficients are mapped to a countable set (in practice, a finite set) of so-called reconstruction levels. The set of reconstruction levels represents a proper subset of the set of possible transform coefficient values. For simplifying the following entropy coding, the admissible reconstruction levels are represented by quantization indexes (also referred to as transform coefficient levels), which are transmitted as part of the bitstream. At the decoder side, the quantization indexes (transform coefficient levels) are mapped to reconstructed transform coefficients. The possible values for the reconstructed transform coefficients correspond to the set of reconstruction levels. At the encoder side, the result of scalar quantization is a block of transform coefficient levels (quantization indexes).

In state-of-the-art video coding standards, uniform reconstruction quantizers (URQs) are used. Their basic design is illustrated in Fig. 3. URQs have the property that the reconstruction levels are equally spaced. The distance D between two neighboring reconstruction levels is referred to as quantization step size. One of the reconstruction levels is equal to 0. Hence, the complete set of available reconstruction levels is uniquely specified by the quantization step size D. The decoder mapping of quantization indexes q to reconstructed transform coefficients t’ is, in principle, given by the simple formula

t' = <7 · D.

In this context, the term“independent scalar quantization” refers to the property that, given the quantization index q for any transform coefficient, the associated reconstructed transform coefficient t’ can be determined independently of all quantization indexes for the other transform coefficients.

Since video decoders typically utilize integer arithmetic with standard precision (e.g., 32 bits), the actual formula used in the standard can slightly differ from the simple multiplication. When neglecting the clipping to the supported dynamic range for the transform coefficients, the reconstructed transform coefficients in H.265 j MPEG-H HEVC are obtained by

where the operators“<<” and“»” represent bit shifts to the left and right, respectively. When we ignore the integer arithmetic, the quantization step size D corresponds to the term

D = scale · 2 sh l ,t

Older video coding standards, such as H.262 | MPEG-2 Video, also specify modified URQs for which the distances between the reconstruction level zero and the first non-zero reconstruction levels are increased relative to the nominal quantization step size (e.g., to three halves of the nominal quantization step size D).

The quantization step size (or the scale and shift parameters) for a transform coefficient is determined by two factors:

• Quantization parameter QP:

The quantization step size can typically be modified on a block basis. For that purpose, video coding standards provide a predefined set of quantization step sizes. The used quantization step size (or, equivalently the parameters“scale” and“shift" introduced above) is indicated using an index into the predefined list of quantization step sizes. The index is called quantization parameter (QP). In H.265 | MPEG-H HEVC, the relationship between QP and the quantization step size is approximately given by

QP

D ~ const - 2 6

A slice QP is typically transmitted in the slice header. In general, it is possible to modify the quantization parameter QP on the basis of blocks. For that purpose, a DQP (delta quantization parameter) can be transmitted. The used quantization parameter is determined by the transmitted DQP and a predicted QP value, which is derived using the QPs of already coded (typically neighboring) blocks.

• Quantization weighting matrix:

Video coding standards often provide the possibility to use different quantization step sizes for individual transform coefficients. This is achieved by specifying so- called quantization weighting matrices w, which can be selected by the encoder, typically on a sequence or picture level, and are transmitted as part of the bitstream. A quantization weighting matrix w has the same size as the

corresponding block of transform coefficients. The quantization step size &ik for a transform coefficient tik is given by

Ajk — wik Ajjjock,

where AWock denotes the quantization step size (indicated by the block quantization parameter QP) for the considered block, /' and k represent the coordinates specifying the current transform coefficient inside the transform block, and wik represents the corresponding entry in the quantization weighting matrix w.

The main intention of quantization weighting matrices is to provide a possibility for introducing the quantization noise in a perceptual meaningful way. By using appropriate weighting matrices, the spatial contrast sensitivity of human vision can be exploited for achieving a better trade-off between bit rate and subjective reconstruction quality. Nonetheless, many encoders use a so-called flat quantization matrix (which can be efficiently transmitted using high-level syntax elements). In this case, the same quantization step size D is used for all transform coefficients in a block. The quantization step size is then completely specified by the quantization parameter QP.

The block of transform coefficient levels (quantization indexes for the transform coefficients) are entropy coded (i.e. , it is transmitted in a lossless manner as part of the bitstream). Since the linear transform can only reduce linear dependencies, the entropy coding for the transform coefficient levels is typically designed in a way that remaining non-linear dependencies between transform coefficient levels in a block can be exploited for an efficient coding. Well known examples are the run-level coding in MPEG-2 Video, the run-level-last coding in H.263 and MPEG-4 Visual, the context-adaptive variable length coding (CAVLC) in H.264 | MPEG-4 AVC, and context-based adaptive binary arithmetic coding (CABAC) in H.264 | MPEG-4 AVC and H.265 | MPEG-H HEVC.

The CABAC specified in the state-of-the-art video coding standard H.265 | MPEG-H HEVC follows a generic concept that can be applied for a large variety of transform block sizes. Transform blocks that are larger than 4x4 samples, such as 10 in Fig. 4a, are partitioned into 4x4 subblocks 12. The partitioning is illustrated in Fig. 4a for the example of a 16x16 transform block 10. The coding order of the 4x4 subblocks as well as the coding order of the transform coefficient levels inside a subblock are, in general, specified by the reverse diagonal scan 14 shown in Fig. 4. For certain intra-picture predicted blocks, a horizontal or vertical scan pattern in used (depending on the actual intra prediction mode). The coding order always starts with high-frequency locations.

In H.265 | MPEG-H HEVC, the transform coefficient levels are transmitted on the basis of

4x4 subblocks. The lossless coding of transform coefficient levels includes the following steps:

1. A syntax element coded jDlockJlag is transmitted, which signals whether there are any non-zero transform coefficient levels in the transform block. If coded_block_flag is equal to 0, no further data are coded for the transform block.

2. The x and y coordinates of the first non-zero transform coefficient level in coding order (e.g., the block-wise reverse diagonal scan order illustrated in Fig. 4) are transmitted. The transmission of the coordinates is split into a prefix and suffix part. The standard uses the syntax elements last_sig_coeff_x_prefix,

I a st_s i g_co eff_y_p ref i x , la st_s i g coeff_x_s uff i x , and last_sig_coeff_x_suffix.

3. Starting with the 4x4 subblock that contains the first non-zero transform coefficient level in coding order, the 4x4 subblocks are processed in coding order, where the coding of a subblock includes the following main steps:

a. A syntax element coded _sub_block_flag is transmitted, which indicates whether the subblock contains any non-zero transform coefficient levels. For the first and last 4x4 subblock (i.e., the subblocks that contain the first non-zero transform coefficient level or the DC level), this flags is not transmitted but inferred to be equal to one.

b. For all transform coefficient levels inside a subblock with coded_sub_blockJ1ag equal to one, the syntax element significant_coeff_flag indicates whether the corresponding transform coefficient level is not equal to zero. This flag is only transmitted if its value cannot be inferred based on already transmitted data. In particular, the flag is not transmitted for the first significant scan position (specified by the transmitted x and y coordinates) and it is not transmitted for the DC coefficient if the DC coefficient is located in a different subblock than the first non-zero coefficient (in coding order) and all other significant_coef lags for the last subblock are equal to zero.

c. For the first eight transform coefficient levels with significant_coeff_flag equal to one (if any), the flag coeff_abs Jevel greaterl _flag is transmitted. It indicates whether the absolute value of the transform coefficient level is greater than one.

d. For the first transform coefficient level with coeff_abs_level_greater1_flag equal to one (if any), the flag coeff_abs_level_greater2_flag is transmitted. It indicates whether the absolute value of the transform coefficient level is greater than two.

e. For all levels with significant_coeff_flag equal to one (an exception is described below), the syntax element coeff_sign__flag is transmitted, which specifies the sign of the transform coefficient level.

f. For all transform coefficient levels for which the absolute value is not already completely specified by the values of significant_coeff_flag, coeff_abs_level _greater1_flag and coeff_abs_level_greater2_flag (the absolute value is completely specified if any of the transmitted flags is equal to zero), the remainder of the absolute value is transmitted using the multi-level syntax element coeff _absjevel_remaining.

In H.265 | MPEG-H HEVC, all syntax elements are coded using context-based adaptive binary arithmetic coding (CABAC). All non-binary syntax elements are first mapped onto a series of binary decisions, which are also referred to as bins. The resulting bin sequence is coded using binary arithmetic coding. For that purpose, each bin is associated with a probability model (binary probability mass function), which is also referred to as a context. For most bins, the context represents an adaptive probability model, which means that the associated binary probability mass function is updated based on the actually coded bin values. Conditional probabilities can be exploited by switching the contexts for certain bins based on already transmitted data. CABAC also includes a so-called bypass mode, in which the fixed probability mass function (0.5, 0.5) is used.

The context that is chosen for the coding of the coded_sub_block_flag depends on the values of coded_sub_block_flag for already coded neighboring subblocks. The context for the significant_coeff_flag is selected based on the scan position (x and y coordinate)

inside a subblock, the size of the transform block, and the values of coded_sub_block_flag in neighboring subblocks. For the flags coeff _absjevel_greater1_flag and coeff_absJevel greater2_flag, the context selection depends on whether the current subblock includes the DC coefficient and whether any coeff_abs_level_greater1_flag equal to one has been transmitted for the neighboring subblocks. For the coeff_abs_level_greater1_flag, it further depends on the number and the values of the already coded coeff_abs_level_greater1_flag’s for the subblock.

The signs coeff_sign_flag and the remainder of the absolute values coeff_abs_level_remaining are coded in the bypass mode of the binary arithmetic coder. For mapping coeff_abs_level_remaining onto a sequence of bins (binary decisions), an adaptive binarization scheme is used. The binarization is controlled by a single parameter (referred to as Rice parameter), which is adapted based on already coded values for the subblock.

H.265 | MPEG-H HEVC also includes a so-called sign data hiding mode, in which (under certain conditions) the transmission of the sign for that last non-zero level inside a subblock is omitted. Instead, the sign for this level is embedded in the parity of the sum of the absolute values for the levels of the corresponding subblock. Note that the encoder has to consider this aspect in determining appropriate transform coefficient levels.

Video coding standards only specify the bitstream syntax and the reconstruction process. If we consider transform coding for a given block of original prediction error samples and given quantization step sizes, the encoder has a lot a freedom. Given the quantization indexes qk for a transform block, the entropy coding has to follow a uniquely defined algorithm for writing the data to the bitstream (i.e., constructing the arithmetic codeword). But the encoder algorithm for obtaining the quantization indexes qk given an original block of prediction error samples is out of the scope of video coding standards. Furthermore, the encoder has the freedom to select a quantization parameter QP on a block basis. For the following description, we assume that the quantization parameter QP and the quantization weighting matrix are given. Hence, the quantization step size for each transform coefficient is known. We further assume that the encoder performs an analysis transform that is the inverse (or a very close approximation of the inverse) of the specified synthesis transform for obtaining original transform coefficients tk. Even under these conditions, the encoder has the freedom to select a quantizer index qk for each original transform coefficient tk. Since the selection of transform coefficient levels determines both the distortion (or reconstruction/approximation quality) and the bit rate, the quantization algorithm used has a substantial impact on the rate-distortion performance of the produced bitstream.

The simplest quantization method rounds the original transform coefficients tk to the nearest reconstruction levels. For the typically used URQs, the corresponding quantization index <¾ can be determined according to

Itk l 1

¾ = sgn(tk) ·

Ak 2

where sgn() is the sign function and the operator )-j returns the largest integer that is smaller or equal to its argument. This quantization methods guarantees that the MSE distortion

is minimized, but it completely ignores the bit rate that is required for transmitting the resulting transform coefficient levels qk. Typically, better results are obtained if the rounding is biased towards zero:

1

<7k = sgn( with 0 < a <

2

The best result in rate-distortion sense is obtained if the quantization process minimizes a Lagrangian function D + A R, where D represent the distortion (e.g., MSE distortion) of the transform block, R specifies the number of bits that are required for transmitting the transform coefficient levels of the block, and A is a Lagrange multiplier.

For codecs that use the relationship D ~ const 2 between QP and quantization step size (such as H.264 | MPEG-4 AVC or H.265 j MPEG-H HEVC), the following relationship between the Lagrange multiplier A and the block quantization parameter QP is often used

, QP

A = Cj D2 = c2- 2 3 ,

where c1 and c2 represent constant factors for a slice or picture.

Quantization algorithms that aim to minimize a Lagrange function D + A R of distortion and rate are also referred to as rate-distortion optimized quantization (RDOQ). If we measure the distortion using the MSE or a weighted MSE, the quantization indexes qk for a transform block should be determined in a way so that the following cost measure is minimized:

At this, the transform coefficient index k specifies the coding order (or scanning order) of transform coefficient levels. The term R(qk \qk-1, qk 2, ··· ) represents the number of bits (or an estimate thereof) that are required for transmitting the quantization index qk. The condition illustrates that (due to the usage of combined or conditional probabilities) the number of bits for a particular transform coefficient level qk typically depends on the chosen values for preceding transform coefficient levels qk-i, qk-2> etc. in coding order. The factors ak in the equation above can be used for weighting the contribution of the individual transform coefficients, e.g., for modelling the contrast sensitivity of human vision. In the following, we generally assume that all weightings factor ak are equal to 1 (but the algorithm can be straightforwardly modified in a way that different weighting factors can be taken into account).

For the transform coefficient coding in H.265 | MPEG-H HEVC, an accurate computation of the rate terms is very complicated, since most binary decisions are coded using adaptive probability models. But if we neglect some aspects of the probability model selection and ignore that the probability models are adapted inside a transform block, it is possible to design an RDOQ algorithm with reasonable complexity. The RDOQ algorithm implemented in the reference software for H.265 | MPEG-H HEVC consists of the following basic processing steps:

1 . For each scanning position k, a transform coefficient level qk is selected by minimizing the Lagrangian cost Dk{qk) + A Rk(qk ) under the assumption that the level is not inferred to be equal to zero. Dk(qk ) denotes the (weighted) squared error Dk {qk) = ak (tk - Ak qkY and Rk (qk ) represents an estimate of the number of bits required for transmitting qk.

2. The flags coded_sub_block_flag for the 4x4 subblocks are determined by comparing the Lagrangian costs for the following two cases: (a) The transform coefficient levels selected in step 1 are used; (b) The syntax element coded_sub_block_flag is set equal to zero and, thus, all transform coefficient levels of the 4x4 subblock are set equal to zero.

3. The location of the first non-zero transform coefficient levels is determined by comparing the Lagrangian costs that are obtained by choosing one of the non-zero transform coefficient levels (after step 2) as first non-zero transform coefficient levels in coding order (the preceding transform coefficient levels are set equal to zero).

4. The coded_block_flag is determined by comparing the Lagrangian costs for the sequence of transform coefficient levels obtained after step 3 and the case that all transform coefficient levels inside the transform block are set equal to zero.

The following describes a modified concept for transform coding. A modification relative to conventional transform coding is that the transform coefficients are not independently quantized and reconstructed. Instead, the admissible reconstruction levels for a transform coefficient depend on the selected quantization indexes for the preceding transform coefficients in reconstruction order. The concept of dependent scalar quantization is combined with a modified entropy coding, in which the probability model selection (or, alternatively, the codeword table selection) for a transform coefficient depends on the set of admissible reconstruction levels.

The advantage of the dependent quantization of transform coefficients is that the admissible reconstruction vectors are denser packed in the /V-dimensional signal space (where N denotes the number of samples or transform coefficients in a transform block). The reconstruction vectors for a transform block refer to the ordered reconstructed transform coefficients (or, alternatively, the ordered reconstructed samples) of a transform block. The effect of dependent scalar quantization is illustrated in Fig. 5 for the simplest case of two transform coefficients. Fig. 5 shows the admissible reconstruction vectors (which represent points in the 2d plane) for independent scalar quantization. As it can be seen, the set of admissible values for the second transform coefficient t{ does not depend on the chosen value for the first reconstructed transform coefficient t0' . Fig. 5b shows an example for dependent scalar quantization. Note that, in contrast to independent scalar quantization, the selectable reconstruction values for the second transform coefficient t[ depend on the chosen reconstruction level for the first transform coefficient t . In the example of Fig. 5b, there are two different sets of available reconstruction levels for the second transform coefficient t (illustrated by different colors). If the quantization index for the first transform coefficient t0' is even (... ,-2,0,2,... ), any reconstruction level of the first set (blue points) can be selected for the second transform coefficient t . And if the quantization index for the first transform coefficient t0' is odd (... ,-3,-1 , 1 ,3, ... ), any reconstruction level of the second set (red points) can be selected for the second transform coefficient t[ . In the example, the reconstruction levels for the first and second set are shifted by half the quantization step size (any reconstruction level of the second set is located between two reconstruction levels of the first set).

The dependent scalar quantization of transform coefficients has the effect that, for a given average number of reconstruction vectors per N-dimensional unit volume 20, the expectation value of the distance between a given input vector of transform coefficients and the nearest available reconstruction vector is reduced. As a consequence, the average distortion between the input vector of transform coefficients and the vector reconstructed transform coefficients can be reduced for a given average number of bits. In vector quantization, this effect is referred to as space-filling gain. Using dependent scalar quantization for transform blocks, a major part of the potential space-filling gain for high dimensional vector quantization can be exploited. And, in contrast to vector quantization, the implementation complexity of the reconstruction process (or decoding process) is comparable to that of conventional transform coding with independent scalar quantizers.

A block diagram of a transform decoder 30 with dependent scalar quantization is illustrated in Fig. 6a and comprises an entropy decoder 32, a quantization stage 34 for dependent scalar dequantization, and a synthesis transformer 36. The main change (highlighted in red) is the dependent quantization. As indicated by the vertical arrows, the reconstructed transform coefficient tk' , with reconstruction order index k > 0, does not only depend on the associated quantization index qk, but also on the quantization indexes q0, q1 , · · · , qk~ \ f°r preceding transform coefficients in reconstruction order. Note that in dependent quantization, the reconstruction order of transform coefficients 13 has to be uniquely defined. The performance of the overall transform codec can typically be improved if the knowledge about the set of reconstruction levels associated with a quantization index qk is also exploited in the entropy coding. That means, it is typically

preferable to switch contexts (probability models) or codeword tables based on the set of reconstruction levels that applies to a transform coefficient.

The transform encoder 40 fitting to the decoder of Fig. 6a is depicted in Fig. 6b and consists of an analysis transform 46, a quantization module 44, and the entropy coder 42. As analysis transform 46 typically the inverse of the synthesis transform (or a close approximation of the inverse) 36 is used, and the entropy coding 42 is usually uniquely specified given the entropy decoding process 32. But, similar as in conventional transform coding, there is a lot of freedom for selecting the quantization indexes given the original transform coefficients.

Embodiments of the present invention are not restricted to block-based transform coding. They are also applicable to transform coding of any finite collection of samples. Any kind of transform that maps a set of samples onto a set of transform coefficients can be used. This includes linear and non-linear transforms. The used transform may also represent an overcomplete (the number of transform coefficients is larger than the number of samples) or undercomplete (the number of transform coefficients is smaller than the number of samples) transform. In an embodiment, the dependent quantization is combined with a linear transform of orthogonal basis functions. Due to integer implementations, the transform can deviate from a linear transform (due to rounding in the transform step). Furthermore, the transform can represent an integer approximation of a transform with orthogonal basis functions (in which case, the basis functions may only be approximately orthogonal). In a special case, the transform can represent the identity transform, in which case the samples or residual samples are directly quantized.

In an embodiment, the dependent quantization concept discussed below is used for transform coding of samples blocks (for example, blocks of original samples or blocks of prediction error samples). In this context, the transform can represent a separable transform, a non-separable transform, a combination of a separable primary transform and a non-separable secondary transform (where the second transform can be either applied to all coefficients obtained after the primary transform or a subset of the coefficients). If a non-separable secondary transform is applied to a subset, the subset can consist of a subblock of the obtained matrix of transform coefficients or it can represent any other subset of the transform coefficients obtained after the primary transform (for example, arbitrarily shaped regions inside the block of transform

coefficients). The transform may also represent a multi-level transform with more than two transform levels.

Details and embodiments for the dependent quantization of transform coefficients are described in the following. Furthermore, various ways for the entropy coding of quantization indexes that specify reconstructed transform coefficients for dependent scalar quantization are described. In addition, optional methods for a block-adaptive selection between dependent and independent scalar quantization as well as methods for adapting the quantization step size are presented. Finally, approaches for determining the quantization indexes for dependent quantization in an encoder are discussed.

Dependent quantization of transform coefficients refers to a concept in which the set of available reconstruction levels for a transform coefficient depends on the chosen quantization indexes for preceding transform coefficients in reconstruction order (typically restricted to quantization indexes inside the same transform block).

In an embodiment, multiple sets of reconstruction levels are pre-defined and, based on the quantization indexes for preceding transform coefficients in coding order, one of the predefined sets is selected for reconstructing the current transform coefficient. Some embodiments for defining sets of reconstruction levels are described first. The identification and signaling of a chosen reconstruction level is described thereinafter. Even further embodiments for selecting one of the pre-defined sets of reconstruction levels for a current transform coefficient (based on chosen quantization indexes for preceding transform coefficients in reconstruction order) are described.

In an embodiment and as is shown in Figs. 7a and 7b, the set 48 of admissible reconstruction levels for a current transform coefficient 13’ is selected 54 (based on a set 58 of the quantization indexes 56 for preceding transform coefficients in coding order 14) among a collection 50 (two or more sets) of pre-defined sets 52 of reconstruction levels. The collection 50 may be used for all coefficients 13, wherein, however, reference is made to the subsequent description of the possible parameterizable characteristic of the sets 52. In a particular embodiment and as shown in subsequent examples, the values of the reconstruction levels of the sets of reconstruction levels are parameterized 60 by a block-based quantization parameter. That means, the video codec supports a modification of a quantization parameter (QP) on the basis of blocks (which can correspond to a single transform block 10 or multiple transform blocks) and the values of the reconstruction

levels inside the used sets of reconstruction levels are determined by the selected quantization parameter. For example, if the quantization parameter is increased, the distance between neighboring reconstruction levels is also increased, and if the quantization parameter is decreased, the distance between neighboring reconstruction levels is also decreased (or vice versa).

In an exemplary version, the block-based quantization parameter (QP) determines a quantization step size D (or corresponding scale and shift parameters as described above) and all reconstruction levels (in all sets of reconstruction levels) represent integer multiples of the quantization step size D. But note that each set of reconstruction levels includes only a subset of the integer multiples of the quantization step size A. Such a configuration for dependent quantization, in which all possible reconstruction levels for all sets of reconstruction levels represent integer multiples of the quantization step size, can be considered of an extension of uniform reconstruction quantizers (URQs). Its basic advantage is that the reconstructed transform coefficients can be calculated by algorithms with a very low computational complexity (as will be described below in more detail).

The sets 52 of the reconstruction levels can be completely disjoint; but it is also possible that one or more reconstruction levels are contained in multiple sets (while the sets still differ in other reconstruction levels). The quantization step size Ak for a particular transform coefficient tk (with k indicating the reconstruction order) may not be solely determined by the block quantization parameter QP, but it is also possible that the quantization step size Ak for a particular transform coefficient tk is determined by a quantization weighting matrix (WM) and the block quantization parameter. Typically, the quantization step size Ak for a transform coefficient tk is given by the product of the weighting factor wk for the transform coefficient tk (specified by the quantization weighting matrix) and the block quantization step size Ab|ock (specified by the block quantization parameter),

It should be noted that the actual calculation, i.e. , the dequantization 62, of reconstructed transform coefficients tk' (or the actual calculation of reconstruction levels) wherein k may index the current position may slightly deviate from an ideal multiplication due to integer implementations or other implementation aspects. Let Ak be the quantization step size for a particular transform coefficient tk and let nk specify a nominal integer factor of the quantization step size (e.g., given by the quantization index qk) With ideal multiplication, the reconstructed transform coefficient tk' is given by

tk' = nk - Ak.

Due to a restriction to integer implementations, the reconstructed transform coefficient tk' (or a corresponding reconstruction level) may be actually determined according to

tk' = (nk scale + (1 « (shift— 1))) » shift, with scale · 2-shlft Ak.

or a similar procedure. If we speak of integer multiples of a quantization step size in the following description, the corresponding text also applies to integer approximations similar to the one specified above. The quantization 64 involves a corresponding division.

In an embodiment, the dependent scalar quantization for transform coefficients uses exactly two different sets 52 of reconstruction levels. And in an embodiment, all reconstruction levels of the two sets for a transform coefficient tk represent integer multiples of the quantization step size Ak for this transform coefficient (which is, at least partly, determined by a block-based quantization parameter). Note that the quantization step size Ak just represents a scaling factor for the admissible reconstruction values in both sets. Except of a possible individual quantization step size Ak for the different transform coefficients tk inside a transform block (and, thus, an individual scaling factor), the same two sets of reconstruction levels are used for all transform coefficients.

In Fig. 8, three configurations for the two sets of reconstruction levels are illustrated. Note that all reconstruction levels lie on a grid given by the integer multiples of the quantization step size D. It should further be noted that certain reconstruction levels can be contained in both sets.

The two sets depicted in Fig. 8a are disjoint. Each integer multiple of the quantization step size D is only contained in one of the sets. While the first set (set 0) contains all even integer multiples of the quantization step size, the second set (set 1 ) contain all odd integer multiples of the quantization step size. In both sets, the distance between any two neighboring reconstruction levels is two times the quantization step size. These two sets are usually suitable for high-rate quantization, i.e. , for settings in which the standard deviation of the transform coefficients is significantly larger than the quantization step size.

In video coding, however, the quantizers are typically operated in a low- rate range. Typically, the absolute value of many original transform coefficients is closer to zero than to any non-zero multiple of the quantization step size. In that case, it is typically preferable if the zero is included in both quantization sets (sets of reconstruction levels).

The two quantization sets illustrated in Fig. 8b both contain the zero. In set 0, the distance between the reconstruction level equal to zero and the first reconstruction level greater than zero is equal to the quantization step size, while all other distances between two neighboring reconstruction levels are equal to two times the quantization step size. Similarly, in set 1 , the distance between the reconstruction level equal to zero and the first reconstruction level smaller than zero is equal to the quantization step size, while all other distances between two neighboring reconstruction levels are equal to two times the quantization step size. Note that both reconstruction sets are non-symmetric around zero. This may lead to inefficiencies, since it makes it difficult to accurately estimate the probability of the sign.

Another configuration for the two sets of reconstruction levels is shown in Fig. 8c. The reconstruction levels that are contained in the first quantization set (labeled as set 0 in the figure) represent the even integer multiples of the quantization step size (note that this set is actually the same as the set 0 in Fig. 8a). The second quantization set (labeled as set 1 in the figure) contains all odd integer multiples of the quantization step size and additionally the reconstruction level equal to zero. Note that both reconstruction sets are symmetric about zero. The reconstruction level equal to zero is contained in both reconstruction sets, otherwise the reconstruction sets are disjoint. The union of both reconstruction sets contains all integer multiples of the quantization step size.

One is not restricted to the configurations shown in Fig. 8. Any other two different sets of reconstruction levels can be used. Multiple reconstruction levels may be included in both sets. Or the union of both quantization sets may not contain all possible integer multiples of the quantization step size. Furthermore, it is possible to use more than two sets of reconstruction levels for the dependent scalar quantization of transform coefficients.

As to the signaling of chosen reconstruction levels, the following is noted. The reconstruction level that the encoder selects in step 64 among the admissible reconstruction levels must be indicated inside the bitstream 14. As in conventional independent scalar quantization, this can be achieved using so-called quantization

indexes 56, which are also referred to as transform coefficient levels. Quantization indexes (or transform coefficient levels) are integer numbers that uniquely identify the available reconstruction levels inside a quantization set 48 (i.e. , inside a set of reconstruction levels). The quantization indexes are sent to the decoder as part of the bitstream 14 (using, e.g., any entropy coding technique). At the decoder side, the reconstructed transform coefficients can be uniquely calculated based on a current set 48 of reconstruction levels (which is determined 54 by the preceding quantization indexes 58 in coding/reconstruction order) and the transmitted quantization index 56 for the current transform coefficient.

In an embodiment, the assignment of quantization indexes to reconstruction levels inside a set 48 of reconstruction levels (or quantization set) follows the following rules. For illustration, the reconstruction levels in Fig. 8 are labeled with an associated quantization index (the quantization indexes are given by the numbers below the circles that represent the reconstruction levels). If a set of reconstruction levels includes the reconstruction level equal to 0, the quantization index equal to 0 is assigned to the reconstruction level equal to 0. The quantization index equal to 1 is assigned to the smallest reconstruction level greater than 0, the quantization index equal to 2 is assigned to the next reconstruction level greater than 0 (i.e., the second smallest reconstruction level greater than 0), etc. Or, in other words, the reconstruction levels greater than 0 are labeled with integer numbers greater than 0 (i.e., with 1 , 2, 3, etc.) in increasing order of their values. Similarly, the quantization index -1 is assigned to the largest reconstruction level smaller than 0, the quantization index -2 is assigned to the next (i.e., the second largest) reconstruction level smaller than 0, etc. Or, in other words, the reconstruction levels smaller than 0 are labeled with integer numbers less than 0 (i.e., -1 , -2, -3, etc.) in decreasing order of their values. For the examples in Fig. 8, the described assignment of quantization indexes is illustrated for all quantization sets, except set 1 in Fig. 8a (which does not include a reconstruction level equal to 0).

For quantization sets that don't include the reconstruction level equal to 0, one way of assigning quantization indexes to reconstruction levels is the following. All reconstruction levels greater than 0 are labeled with quantization indexes greater than 0 (in increasing order of their values) and all reconstruction levels smaller than 0 are labeled with quantization indexes smaller than 0 (in decreasing order of the values). Hence, the assignment of quantization indexes basically follows the same concept as for quantization sets that include the reconstruction level equal to 0, with the difference that there is no quantization index equal to 0 (see labels for quantization set 1 in Fig. 8a). That aspect should be considered in the entropy coding of quantization indexes. For example, the quantization index is often transmitted by coding its absolute value (ranging from 0 to the maximum supported value) and, for absolute values unequal to 0, additionally coding the sign of the quantization index. If no quantization index equal to 0 is available, the entropy coding could be modified in a way that the absolute level minus 1 is transmitted (the values for the corresponding syntax element range from 0 to a maximum supported value) and the sign is always transmitted. As an alternative, the assignment rule for assigning quantization indexes to reconstruction levels could be modified. For example, one of the reconstruction levels close to zero could be labeled with the quantization index equal to 0. And then, the remaining reconstruction levels are labeled by the following rule: Quantization indexes greater than 0 are assigned to the reconstruction levels that are greater than the reconstruction level with quantization index equal to 0 (the quantization indexes increase with the value of the reconstruction level). And quantization indexes less than 0 are assigned to the reconstruction levels that are smaller than the reconstruction level with the quantization index equal to 0 (the quantization indexes decrease with the value of the reconstruction level). One possibility for such an assignment is illustrated by the numbers in parentheses in Fig. 8a (if no number in parentheses is given, the other number applies).

As mentioned above, in an embodiment, two different sets 52 of reconstruction levels (which we also call quantization sets) are used, and the reconstruction levels inside both sets 52 represent integer multiples of the quantization step size D. That includes cases, in which different quantization step sizes are used for different transform coefficients inside a transform block (e.g., by specifying a quantization weighting matrix). And it includes cases, in which the quantization step size is modified on a block basis (e.g., by transmitting a block quantization parameter inside the bitstream).

The usage of reconstruction levels that represent integer multiples of a quantization step sizes allows computationally low complex algorithms for the reconstruction of transform coefficients at the decoder side. This is illustrated based on the example of Fig. 8c in the following (similar simple algorithms also exist for other configurations, in particular, the settings shown in Fig. 8a and Fig. 8b). In the configuration shown in Fig. 8c, the first quantization set, set 0, includes all even integer multiples of the quantization step size and the second quantization set, set 1 , includes all odd integer multiples of the quantization step size plus the reconstruction level equal to 0 (which is contained in both quantization sets). The reconstruction process 62 for a transform coefficient 13’ could be implemented similar to the algorithm specified in the pseudo-code of Fig. 9.

In the pseudo-code of Fig. 9a, level[k] denotes the quantization index 56 that is transmitted in the data stream 14 for a transform coefficient tk 13’ and setld[k] (being equal to 0 or 1 ) specifies the identifier of the current set 48 of reconstruction levels (it is determined 54 based on preceding quantization indexes 58 in reconstruction order 14 as will be described in more detail below). The variable n represents the integer multiple of the quantization step size given by the quantization index level[k] and the set identifier setld[k]. If the transform coefficient 13’ is coded using the first set of reconstruction levels (setld[k] == 0), which contains the even integer multiples of the quantization step size &k , the variable n is two times the transmitted quantization index. If the transform coefficient 13’ is coded using the second set of reconstruction levels (setld[k] == 1 ), we have the following three cases: (a) if levei[k] is equal to 0, n is also equal to 0; (b) if level [k] is greater than 0, n is equal to two times the quantization index level[k] minus 1 ; and (c) if level[k] is less than 0, n is equal to two times the quantization index level[k] plus 1. This can be specified using the sign function

sign

Then, if the second quantization set is used, the variable n is equal to two times the quantization index levei[k] minus the sign function sign(level[k]) of the quantization index.

Once the variable n (specifying the integer factor of the quantization step size) is determined, the reconstructed transform coefficient tk' , i.e. the current coefficient 13’, is obtained by multiplying n with the quantization step size Ak .

As mentioned above, instead of an exact multiplication with the quantization step size Ak, the reconstructed transform coefficient tk' can be obtained by an integer approximation. This is illustrated in the pseudo-code in Fig. 9b. Here, the variable“shift” represents a bit shift to the right. Its value typically depends only on the quantization parameter for the block (but it is also possible that the shift parameter can be changed for different transform coefficients inside a block). The variable scale[k] represents a scaling factor for the transform coefficient tk ; in addition to the block quantization parameter, it can, for example, depend on the corresponding entry of the quantization weighting matrix. The

variable“add” specifies a rounding offset, it is typically set equal to add = (1 «(shift-1 )). It should be noted that the integer arithmetic specified in the pseudo-code of Fig. 9b (last line) is, with exception of the rounding, equivalent to a multiplication with a quantization step size Ak, given by

D!( = scale[fc] · 2 sh l ft.

Another change in Fig. 9b relative to Fig. 9a is that the switch between the two sets of reconstruction levels is implemented using the ternary if-then-else operator ( a ? b : c ), which is known from programming languages such as the C programming language, which change could be reversed, however, or applied to Fig. 9a, too.

Besides the selection of the sets 52 of reconstruction levels discussed above, another important design aspect of dependent scalar quantization is the algorithm 54 used for switching between the defined quantization sets (sets of reconstruction levels). The algorithm 54 used determines the “packing density” that can be achieved in the N-dimensional space 20 of transform coefficients (and, thus, also in the /V-dimensional space 20 of reconstructed samples). A higher packing density eventually results in an increased coding efficiency. The selection process 54 may use a fixed selection rule for each transform coefficient, where the chosen set of reconstruction levels depends on a certain number of directly preceding reconstruction levels in coding order as described below (where this number is exemplarily two). This rule may be implemented using state transition tables or a trellis structure as further outlined below.

A particular exemplary way of determining 54 the set 48 of reconstruction levels for the next transform coefficients is based on a partitioning of the quantization sets 52, as it is illustrated in Fig. 10a for a particular example. Note that the quantization sets shown in Fig. 10a are the same quantization sets as the ones in Fig. 8c. Each of the two (or more) quantization sets 52 is partitioned into two subsets. For the example in Fig. 10a, the first quantization set (labeled as set 0) is partitioned into two subsets (which are labeled as A and B) and the second quantization set (labeled as set 1 ) is also partitioned into two subsets (which are labeled as C and D). Even though it is not the only possibility (an alternative is described below), the partitioning for each quantization set is exemplarily done in a way that directly neighboring reconstruction levels (and, thus, neighboring quantization indexes) are associated with different subsets. In an embodiment, each

quantization set is partitioned into two subsets. In Fig. 8 and Fig. 10a, the partitioning of the quantization sets into subsets is indicated by hollow and filled circles.

For the embodiment illustrated in Fig. 10a and Fig. 8c, the following partitioning rules apply:

• Subset A consists of all even quantization indexes of the quantization set 0;

• Subset B consists of all odd quantization indexes of the quantization set 0;

• Subset C consists of all even quantization indexes of the quantization set 1 ;

• Subset D consists of all odd quantization indexes of the quantization set 1.

It should be noted that the used subset is typically not explicitly indicated inside the bitstream. Instead, it can be derived based on the used quantization set (e.g., set 0 or set 1 ) and the actually transmitted quantization index. For the partitioning shown in Fig. 10a, the subset can be derived by a bit-wise“and” operation of the transmitted quantization index level and 1. Subset A consists of all quantization indexes of set 0 for which

(level&1 ) is equal to 0, subset B consists of all quantization indexes of set 0 for which

(!evel&1 ) is equal to 1 , subset C consists of all quantization indexes of set 1 for which

(level&1 ) is equal to 0, and subset D consists of all quantization indexes of set 1 for which

(level&1 ) is equal to 1.

In an embodiment, the quantization set (set of admissible reconstruction levels) that is used for reconstructing a current transform coefficient is determined based on the subsets that are associated with the last two or more quantization indexes. An example, in which the two last subsets (which are given by the last two quantization indexes) are used is shown in the table of Fig. 10b. The table has to be read as follows: The first subset given in the first table column represents the subset for the directly preceding coefficient and the second subset given in the first table column represents the subset for the coefficient that precedes the directly preceding coefficient. The determination 54 of the quantization set 48 specified by this table represents a specific embodiment. In other embodiments, the quantization set 48 for a current transform coefficient 13’ is determined by the subsets that are associated with the last three or more quantization indexes 58. For the first transform coefficient of a transform block, we don’t have any data about the subsets of preceding transform coefficients (since there are no preceding transform coefficients). In an embodiment, pre-defined values are used in these cases. In a further embodiment, we infer the subset A for all non-available transform coefficients.

Claims

1. Apparatus for decoding a media signal from a data stream, configured to

sequentially decode a sequence of samples (13) which describe the media signal by

selecting (54), for a current sample (13’), a set (48) of reconstruction levels out of a plurality (50) of reconstruction level sets (52) depending on quantization indices (58) decoded from the data stream (14) for previous samples of the sequence of samples,

entropy decoding a quantization index (56) for the current sample (13’) from the data stream (14), wherein the quantization index (56) indicates one reconstruction level out of the selected set (48) of reconstruction levels for the current sample,

dequantizing (62) the current sample (13’) onto the one reconstruction level of the selected set (48) of reconstruction levels that is indicated by the quantization index (56) for the current sample.

2. Apparatus of claim 1 , wherein the media signal comprises a picture (212).

3. Apparatus of claim 1 or 2, wherein the media signal comprises a picture (212) and the sequence of samples represents transform coefficients of a transform coefficient block (10), scanned along a predetermined coefficient scan (14) so that the decoding the sequence of samples yields the transform coefficient block (10), wherein the apparatus is configured to

subject (36) the transform coefficient block (10) to an inverse transformation to obtain a picture block (284) of the picture (212).

4. Apparatus of claim 1 or 2, the media signal comprises a picture (212) and the apparatus is configured to

predict the picture content of the picture (212) within a picture block (284) of the picture, wherein the sequence of samples (13) represents picture samples of a prediction residual of the prediction of the picture content of the picture within the picture block so that the decoding the sequence of samples yields the picture samples of the prediction residual, wherein the apparatus is configured to

combine the picture samples of the prediction residual with the prediction of the picture content of the picture within the picture block to obtain a block of reconstructed samples for the picture.

5. Apparatus of claim 1 or 2, the media signal comprises a picture (212) and the apparatus is configured to

predict the picture content of the picture (212) within a picture block (284) of the picture,

wherein the sequence of samples (13) is formed by transform coefficients of a transform coefficient block (10), scanned along a predetermined coefficient scan (14), so that the decoding the sequence of samples yields the transform coefficient block (10), and the apparatus is configured to

subject (36) the transform coefficient block (10) to an inverse transformation to obtain a block of residual samples, and

combine the block of residual samples with the prediction of the picture content of the picture within the picture block to reconstruct the picture within the picture block.

6. Apparatus of claim 5, configured to

perform the combination by adding the block of residual samples and the prediction of the picture content of the picture within the picture block.

7. Apparatus of claims 4 to 6, configured to perform the prediction of the picture content of the picture within the picture block by

intra- prediction, or

inter prediction.

8. Apparatus of claim 7, wherein the data stream includes an indication indicating whether intra- or inter prediction is used.

9. Apparatus of any of claims 1 to 8, wherein the number of reconstruction level sets (52) of the plurality (50) of reconstruction level sets (52) is two.

10. Apparatus of any of claims 1 to 9, configured to

parametrize the plurality (50) of reconstruction level sets (52) by way of a predetermined quantization step size and derive information on the predetermined quantization step size from the data stream (14).

1 1. Apparatus of any of claims 1 to 10, wherein the media signal comprises a picture (212) and the apparatus is configured to

partition the picture (212) into picture blocks (284) and

derive information on a predetermined quantization step size from the data stream (14) in a manner varying among the picture blocks, and

parametrize the plurality (50) of quantization level sets (52) by way of the predetermined quantization step size.

12. Apparatus of any of claims 1 to 1 1 , wherein the media signal comprises a picture (212) and the apparatus is configured to

partition the picture (212) into picture blocks (284) and

reconstruct each of a subset of the picture blocks (284) of the picture (212) by way of an inverse transformation of a transform coefficient block (10), wherein the transform coefficients of a predetermined transform coefficient block, scanned along a predetermined coefficient scan, form the sequence of samples, and

derive information on a predetermined quantization step size from the data stream (14) in a manner varying among subset of transform blocks, and

parametrize the plurality (50) of quantization level sets (52) by way of the predetermined quantization step size.

13. Apparatus of claim 11 or 12, wherein the predetermined quantization step size is defined by a quantization parameter that applies to a single picture block or a group of picture blocks and the quantization parameter is derived by

predicting a quantization parameter for a predetermined picture block based on quantization parameters of neighboring picture blocks;

entropy decoding a quantization parameter difference for the predetermined picture block or group of picture blocks from the data stream;

adding the quantization parameter difference to the prediction of the quantization parameter to obtain the quantization parameter for the predetermined picture block or group of picture blocks.

14. Apparatus of claims 12 or 13, wherein the sequence of samples are transform coefficients of a transform coefficient block, scanned along a predetermined coefficient scan (14), and the apparatus is configured to

derive a base quantization step size for the transform coefficient block from the data stream,

derive scale information from the data stream which defines as to how the base quantization step size is scaled in order to obtain quantization step sizes for the transform coefficients of the transform coefficient block (10) so that the quantization step sizes vary across the transform coefficient locations inside the transform coefficient block (10),

parametrize the plurality (50) of quantization level sets (52) by a predetermined quantization step size obtained by scaling the base quantization step size according to the scale information.

15. Apparatus of any of claims 9 to 13, wherein each of the plurality (50) of reconstruction level sets (52) for the current sample consists of integer multiples of a predetermined quantization step size, wherein the quantization step size is the same for all reconstruction level sets of the plurality (50) of reconstruction level sets (52) for the current sample.

16. Apparatus of any of claims 1 to 15, wherein the number of reconstruction level sets (52) of the plurality (50) of reconstruction level sets (52) is two and the plurality of quantization level sets comprises

a first reconstruction level set that comprises zero and even multiples of a predetermined quantization step size, and

a second reconstruction level set that comprises zero and odd multiples of the predetermined quantization step size.

17. Apparatus of claims 1 to 16, wherein all reconstruction levels of all reconstruction level sets represent integer multiples of a predetermined quantization step size, and the apparatus is configured to dequantize the samples by

deriving, for each sample, an intermediate integer value depending on the selected reconstruction level set for the respective sample and the entropy decoded quantization index for the respective sample, and

multiplying, for each sample, the intermediate value for the respective sample with the predetermined quantization step size for the respective sample.

18. Apparatus of claim 17, wherein the number of reconstruction level sets (52) of the plurality (50) of reconstruction level sets (52) is two and the apparatus is configured to derive the intermediate value for each sample by,

if the selected reconstruction level set for the respective sample is a first set, multiply the quantization index for the respective sample by two to obtain the intermediate value for the respective sample; and

if the selected reconstruction level set for a respective sample is a second set and the quantization index for the respective sample is equal to zero, set the intermediate value for the respective sample equal to zero; and

if the selected reconstruction level set for a respective sample is a second set and the quantization index for the respective sample is greater than zero, multiply the quantization index for the respective sample by two and subtract one from the result of the multiplication to obtain the intermediate value for the respective sample; and

if the selected reconstruction level set for a current sample is a second set and the quantization index for the respective sample is less than zero, multiply the quantization index for the respective sample by two and add one to the result of the multiplication to obtain the intermediate value for the respective sample.

19. Apparatus of any of claims 1 to 18, configured to

select (54), for the current sample (13’), the set (48) of reconstruction levels out of the plurality (50) of reconstruction level sets (52) depending on a LSB portion or previously decoded bins of a binarization of the quantization indices (58) decoded from the data stream (14) for previous samples of the sequence of samples.

20. Apparatus of any of claims 1 to 18, configured to

select (54), for the current sample (13’), the set (48) of reconstruction levels out of the plurality (50) of reconstruction level sets (52) depending on the results of a binary function of the quantization indices (58) decoded from the data stream (14) for previous samples of the sequence of samples.

21. Apparatus of any of claims 1 to 20, wherein the apparatus is configured to

select (54), for the current sample (13’), the set (48) of quantization levels out of the plurality (50) of quantization level sets (52) depending on a parity of the quantization indices (56) decoded from the data stream (14) for previous samples of the sequence of samples.

22. Apparatus of any of claims 1 to 21 , wherein the number of reconstruction level sets (52) of the plurality (50) of reconstruction level sets (52) is two, and the apparatus is configured to

derive a subset index for each sample based on the selected set of reconstruction levels for the respective sample and a binary function of the quantization index for the respective sample, resulting in four possible values for the subset index; and

select (54), for the current sample (13’), the set (48) of reconstruction levels out of the plurality (50) of reconstruction level sets (52) depending on the subset indices for previous samples of the sequence of samples.

23. Apparatus of claims 22, wherein the apparatus is configured to

select (54), for the current sample (13’), the set (48) of reconstruction levels out of the plurality (50) of reconstruction level sets (52) using a selection rule which depends on the subset indices for a number of immediately preceding samples of the sequence of samples and to use the selection rule for all, or a portion, of the sequence of samples.

24. Apparatus of claim 23, wherein the number of immediately preceding samples of the sequence of samples on which the selection rule depends is two.

25. Apparatus of any of claims 22 to 24, wherein the subset index for each sample is derived based on the selected set of reconstruction levels for the sample and a parity of the quantization index for the sample.

26. Apparatus of any of claims 19 to 25, wherein the selection rule for selecting a reconstruction level set out of a plurality of reconstruction level sets is realized via a state transition process, in such a way that

a state associated with the current sample (13’) uniquely determines the set (48) of reconstruction levels used for the current sample (13'),

and the state for the current sample depends on the state for an immediately preceding sample of the sequence of samples and a quantization index (58) decoded from the data stream for the immediately preceding sample of the sequence of samples.

27. Apparatus of claim 26, wherein the state for the current sample depends on the state for an immediately preceding sample of the sequence of samples and a binary function of the quantization index (58) decoded from the data stream for the immediately preceding sample of the sequence of samples.

28. Apparatus of claim 27, wherein the state for the current sample depends on the state for an immediately preceding sample of the sequence of samples and a parity of the quantization index (58) decoded from the data stream for the immediately preceding sample of the sequence of samples.

29. Apparatus of any of claims 26 to 28, wherein the number of possible states is four.

30. Apparatus of claim 29, wherein the number of reconstruction level sets (52) of the plurality (50) of reconstruction level sets (52) is two and the apparatus is configured to, with the possible states being numbered from 0 to 3, both inclusively,

select a first reconstruction level set for a current sample if the state for the current sample is 0 or 1 ; and

select a second reconstruction level set for a current sample if the state for the current sample is 2 or 3.

31. Apparatus of claim 30, wherein the apparatus is configured to perform a state transition of the state transition process by

setting the state for the current sample equal to 0, if the state for the preceding sample is equal to 0 and the parity of the preceding quantization index is equal to 0, or if the state for the preceding sample is equal to 1 and the parity of the preceding quantization index is equal to 1 ; and

setting the state for the current sample equal to 1 , if the state for the preceding sample is equal to 2 and the parity of the preceding quantization index is equal to 0, or if the state for the preceding sample is equal to 3 and the parity of the preceding quantization index is equal to 1 ; and

setting the state for the current sample equal to 2, if the state for the preceding sample is equal to 1 and the parity of the preceding quantization index is equal to 0, or if the state for the preceding sample is equal to 0 and the parity of the preceding quantization index is equal to 1 ; and

setting the state for the current sample equal to 3, if the state for the preceding sample is equal to 3 and the parity of the preceding quantization index is equal to 0, or if the state for the preceding sample is equal to 2 and the parity of the preceding quantization index is equal to 1.

32. Apparatus of any of claims 26 to 31 , wherein a state for the first - in an order of the sequence of samples - sample of the sequence of samples is set equal to a predefined value.

33. Apparatus of claims 32, wherein the state for the first sample of the sequence of samples is set equal to zero.

34. Apparatus of any of claims 1 to 33, configured to

decode the quantization index (56) for the current sample (13’) in form of

an absolute value which is indicative of the absolute of

a rank distance between a rank of zero and a rank of the one reconstruction level when ordering the selected set of quantization levels according to their values and, if zero is not included in the selected set of quantization levels, a rank distance between the rank of the one level and a rank of a smallest level of equal sign, when ordering the set of quantization levels according to their values, plus one, or

a rank distance between a rank of a predetermined level in the set of quantization levels which is of minimum absolute value, and a rank of the one reconstruction level when ordering the set of quantization levels according to their values, and

if the absolute value is greater than zero, a sign value which is indicative of the sign of the one reconstruction level.

35. Apparatus of any of claims 1 to 34, wherein

the number of reconstruction level sets (52) of the plurality (50) of reconstruction level sets (52) is two, and a first reconstruction level set of the plurality (50) of reconstruction level sets (52) comprises zero and even multiples of a predetermined quantization step size, and a second reconstruction level set of the plurality (50) of reconstruction level sets (52) comprises zero and odd multiples of the predetermined quantization step size, and

the apparatus configured to decode the quantization index (56) for the current sample (13’) in form of an absolute value and, if the absolute value is greater than zero, a sign value which is indicative of the sign of the one reconstruction level, and determine the reconstruction level from the absolute value and the sign value to be a first function applied to the absolute value and the sign if the selected reconstruction level set is the first reconstruction level set and a second function applied to the absolute value and the sign, if the selected reconstruction level set is the second reconstruction level set, with the first and second functions being symmetric with respect to the absolute value and reconstruction level.

36. Apparatus of any of claims 1 to 35, configured to

decode an absolute value of the quantization index (56) for the current sample (13’) from the data stream using a binarization of the absolute value which comprises

a first bin which specifies whether the absolute value is greater than zero or not.

37. Apparatus of claim 36, wherein the binarization of the absolute value further comprises a second bin (100) which specifies whether the absolute value is greater than one or not, where the second bin is only included in the data stream if the first bin indicates that the absolute value is greater than zero.

38. Apparatus of claims 36 or 37, wherein the binarization of the absolute value further comprises a further bin (100) which specifies a parity of the absolute value, where the further bin is only included in the data stream if the first bin indicates that the absolute value is greater than zero

39. Apparatus of claim 37, wherein the binarization of the absolute value further comprises a further bin (100) which specifies a parity of the absolute value, where the further bin is only included in the data stream if the second bin is included in the data stream and the second bin indicates that the absolute value is greater than one.

40. Apparatus of any of claims 1 to 39, configured to

decode an absolute value of the quantization index (56) for the current sample (13’) from the data stream using binary arithmetic decoding by

entropy decoding (85’) a first bin of a bin string onto which the absolute value is binarized, which first bin specifies whether the absolute value is greater than zero or not, using a first adaptive probability model, wherein the probability model is selected among a set of adaptive probability models and the selection depends on the set (48) of reconstruction values selected for the current sample (13’).

41. Apparatus of any of claims 26 to 39, configured to

decode an absolute value of the quantization index (56) for the current sample (13’) from the data stream using binary arithmetic decoding by

entropy decoding (85’) a first bin of a bin string onto which the absolute value is binarized, which first bin specifies whether the absolute value is greater than zero or not, using a first adaptive probability model, wherein the probability model is selected among a set of adaptive probability models and the selection depends on the state for the current sample.

42. Apparatus of claim 40 or 41 , configured so that

the selection of the first adaptive probability model further depends on a parity of the quantization index for an immediately preceding sample of the sequence of samples.

43. Apparatus of any of claims 40 to 42, configured so that

the selection of the first adaptive probability model further depends on one or more preceding samples of the sequence of samples having a predetermined locational relationship to the current sample.

44. Apparatus of claim 43, wherein

the sequence of samples are transform coefficients of a transform coefficient block (10), and

the locational relationship to the current sample is determined by a template (122) positioned at the current sample (13’).

45. Apparatus of claim 44, wherein

the selection of the first adaptive probability model depends on

the sum of absolute quantization indices inside the template; and/or

the number of non-zero absolute quantization indices inside the template.

46. Apparatus of any of claims 1 to 45, configured to

decode an absolute value of the quantization index (56) for the current sample (13’) from the data stream using binary arithmetic decoding by

entropy decode (85’) a second bin of the a string onto which the absolute value is binarized, which second bin specifies whether the absolute value is greater than one or not, using a second adaptive probability model, wherein the probability model is selected among a set of adaptive probability models and the selection depends on the set (48) of quantization values selected for the current sample (13’) or the state for the current sample.

47. Apparatus of claim 46, configured so that

the selection of the second adaptive probability model further depends on a parity of an immediately preceding sample of the sequence of samples.

48. Apparatus of any of claims 37 to 47, configured so that

the selection of the second adaptive probability model depends on one or more immediately preceding samples of the sequence of samples having a predetermined locational relationship to the current sample.

49. Apparatus of claim 48, wherein

the sequence of samples are transform coefficients of a transform coefficient block (10), and

the locational relationship to the current sample is determined by a template (122) positioned at the current sample (13’).

50. Apparatus of any of claims 36 to 49, wherein the apparatus is configured to

entropy decode the bins of the binarization of the absolute values of the quantization indexes in multiple passes over the scanning positions of the block or subblocks of the block.

51. Apparatus of claim 49, configured to perform a probability model selection for a currently decoded bin of the absolute value of the quantization index among a set of adaptive probability models depending on already decoded bins of the binarization of quantization indices of other samples of the sequence of samples.

52. Apparatus of any of claims 1 to 51 , configured to

sequentially decode a further sequence of samples (13) which describe the media signal by use of mutually sample independent scalar dequantization.

53. Apparatus of any of claims 1 to 52, wherein the media signal is a video sequence of pictures and the samples of the sequence of samples are transform coefficients of a transform coefficient block (10) representing a transform of a predetermined transform block of transform bocks (284) the video pictures are partitioned into, wherein the apparatus switches between decoding the transform blocks by use of mutually sample independent scalar dequantization and mutually sample dependent scalar dequantization as applied for the predetermined transform block.

54. Apparatus of claim 53, configured to perform the switching between the decoding the transform blocks by use of mutually sample independent scalar dequantization and mutually sample dependent scalar dequantization depending on one or more coding parameters signaled in the data stream and varying across the picture or between pictures of the video sequence.

55. Apparatus of claim 53 or 54, configured to derive the switching between the decoding the transform blocks by use of mutually sample independent scalar dequantization and mutually sample dependent scalar dequantization from explicit signaling in the data stream.

56. Method for decoding a media signal from a data stream, comprising

sequentially decoding a sequence of samples (13) which describe the media signal by

selecting (54), for a current sample (13’), a set (48) of reconstruction levels out of a plurality (50) of reconstruction level sets (52) depending on quantization indices (58) decoded from the data stream (14) for previous samples of the sequence of samples,

entropy decoding a quantization index (56) for the current sample (13’) from the data stream (14), wherein the quantization index (56) indicates one reconstruction level out of the selected set (48) of reconstruction levels for the current sample,

dequantizing (62) the current sample (13’) onto the one reconstruction level of the selected set (48) of reconstruction levels that is indicated by the quantization index (56) for the current sample.

57. Apparatus for encoding a media signal into a data stream, configured to

sequentially encode a sequence of samples (13) which describe the media signal by

selecting (54), for a current sample (13’), a set (48) of reconstruction levels out of a plurality (50) of reconstruction level sets (52) depending on quantization indices (58) encoded into the data stream (14) for previous samples of the sequence of samples,

quantizing (64) the current sample (13’) onto one reconstruction level of the set (48) of reconstruction levels, and

encoding a quantization index (56) which indicates the one reconstruction level out of the selected set (48) of reconstruction levels for the current sample (13’) into the data stream (14).

58. Apparatus of claim 57, wherein the media signal comprises a picture (212).

59. Apparatus of claim 57 or 58, wherein the media signal comprises a picture (212) and the apparatus is configured to

transform a picture block (284) of the picture (212) to obtain a transform coefficient block (10), wherein predetermined transform coefficients of the transform coefficient block, scanned along a predetermined coefficient scan (14) represent the sequence of samples.

60. Apparatus of claim 57 or 58, the media signal comprises a picture (212) and the apparatus is configured to

predict the picture content of the picture (212) within a picture block (284) of the picture, wherein the sequence of samples (13) represents picture samples of a prediction residual of the prediction of the picture content of the picture within the picture block so that a decoding the sequence of samples yields the picture samples of the prediction residual and combining the picture samples of the prediction residual with the prediction of the picture content of the picture within the picture block yields a block of reconstructed samples for the picture.

61. Apparatus of claim 57 or 58, the media signal comprises a picture (212) and the apparatus is configured to

predict the picture content of the picture (212) within a picture block (284) of the picture,

wherein the sequence of samples (13) is formed by transform coefficients of a transform coefficient block (10), scanned along a predetermined coefficient scan (14), so that the decoding the sequence of samples yields the transform coefficient block (10), and the apparatus is configured to determine the transform coefficient block (10) so that

subjecting (36) the transform coefficient block (10) to an inverse transformation yields a block of residual samples, and

combining the block of residual samples with the prediction of the picture content of the picture within the picture block yields a reconstruction of the picture within the picture block.

62. Apparatus of claim 61 , wherein

the combination involves adding the block of residual samples and the prediction of the picture content of the picture within the picture block.

63. Apparatus of claims 60 to 62, configured to perform the prediction of the picture content of the picture within the picture block by

intra- prediction, or

inter prediction.

64. Apparatus of claim 63, configured to provide the data stream with an indication indicating whether intra- or inter prediction is used.

65. Apparatus of any of claims 57 to 64, wherein the number of reconstruction level sets (52) of the plurality (50) of reconstruction level sets (52) is two.

66. Apparatus of any of claims 57 to 65, configured to

parametrize the plurality (50) of reconstructionlevel sets (52) by way of a predetermined quantization step size and insert information on the predetermined quantization step size into the data stream (14).

67. Apparatus of any of claims 57 to 66, wherein the media signal comprises a picture (212) and the apparatus is configured to

partition the picture (212) into picture blocks (284) and

insert information on a predetermined quantization step size into the data stream (14) in a manner varying among the picture blocks, and

parametrize the plurality (50) of quantization level sets (52) by way of the predetermined quantization step size.

68. Apparatus of any of claims 57 to 67, wherein the media signal comprises a picture (212) and the apparatus is configured to

partition the picture (212) into picture blocks (284) and

determine for each of a subset of the picture blocks (284) of the picture (212) a transform coefficient block (10), wherein the transform coefficients of a predetermined transform coefficient block, scanned along a predetermined coefficient scan, form the sequence of samples, and determine for each of a subset of the picture blocks (284) of the picture (212) the transform coefficient block (10) such that same is reconstructable by way of an inverse transformation of the transform coefficient block (10),

insert information on a predetermined quantization step size into the data stream (14) in a manner varying among subset of transform blocks, and

parametrize the plurality (50) of quantization level sets (52) by way of the predetermined quantization step size.

69. Apparatus of claim 67 or 68, wherein the predetermined quantization step size is defined by a quantization parameter that applies to a single picture block or a group of picture blocks and the quantization parameter is encoded by

predicting a quantization parameter for a predetermined picture block based on quantization parameters of neighboring picture blocks;

entropy encoding a quantization parameter difference for the predetermined picture block or group of picture blocks into the data stream;

wherein adding the quantization parameter difference to the prediction of the quantization parameter yields the quantization parameter for the predetermined picture block or group of picture blocks.

70. Apparatus of claims 68 or 69, wherein the sequence of samples are transform coefficients of a transform coefficient block, scanned along a predetermined coefficient scan (14), and the apparatus is configured to

insert a base quantization step size for the transform coefficient block into the data stream,

insert scale information into the data stream which defines as to how the base quantization step size is scaled in order to obtain quantization step sizes for the transform coefficients of the transform coefficient block (10) so that the quantization step sizes vary across the transform coefficient locations inside the transform coefficient block (10),

wherein the plurality (50) of quantization level sets (52) are parametrized by a predetermined quantization step size obtained by scaling the base quantization step size according to the scale information.

71. Apparatus of any of claims 65 to 69, wherein each of the plurality (50) of reconstruction level sets (52) for the current sample consists of integer multiples of a predetermined quantization step size, wherein the quantization step size is the same for all reconstruction level sets of the plurality (50) of reconstruction level sets (52) for the current sample.

72. Apparatus of any of claims 57 to 71 , wherein the number of reconstruction level sets (52) of the plurality (50) of reconstruction level sets (52) is two and the plurality of quantization level sets comprises

a first reconstruction level set that comprises zero and even multiples of a predetermined quantization step size, and

a second reconstruction level set that comprises zero and odd multiples of the predetermined quantization step size.

73. Apparatus of claims 57 to 72, wherein all reconstruction levels of all reconstruction level sets represent integer multiples of a predetermined quantization step size, and the apparatus is configured to quantize each sample

to a product of an intermediate integer value derivable depending on the selected reconstruction level set for the respective sample and the entropy decoded quantization index for the respective sample,

and the predetermined quantization step size for the respective sample.

74. Apparatus of claim 73, wherein the number of reconstruction level sets (52) of the plurality (50) of reconstruction level sets (52) is two and the apparatus is configured so that the intermediate value for each sample is derivable by,

if the selected reconstruction level set for the respective sample is a first set, multiply the quantization index for the respective sample by two to obtain the intermediate value for the respective sample; and

if the selected reconstruction level set for a respective sample is a second set and the quantization index for the respective sample is equal to zero, set the intermediate value for the respective sample equal to zero; and

if the selected reconstruction level set for a respective sample is a second set and the quantization index for the respective sample is greater than zero, multiply the quantization index for the respective sample by two and subtract one from the result of the multiplication to obtain the intermediate value for the respective sample; and

if the selected reconstruction level set for a current sample is a second set and the quantization index for the respective sample is less than zero, multiply the quantization index for the respective sample by two and add one to the result of the multiplication to obtain the intermediate value for the respective sample.

75. Apparatus of any of claims 57 to 74, configured to

select (54), for the current sample (13’), the set (48) of reconstruction levels out of the plurality (50) of reconstruction level sets (52) depending on a LSB portion of the quantization indices (58) or previously coded bins of a binarization of the quantization indices (58) encoded into the data stream (14) for previous samples of the sequence of samples.

76. Apparatus of any of claims 57 to 75, configured to

select (54), for the current sample (13’), the set (48) of reconstruction levels out of the plurality (50) of reconstruction level sets (52) depending on the results of a binary function of the quantization indices (58) encoded into the data stream (14) for previous samples of the sequence of samples.

77. Apparatus of any of claims 57 to 76, wherein the apparatus is configured to

select (54), for the current sample (13’), the set (48) of quantization levels out of the plurality (50) of quantization level sets (52) depending on a parity of the quantization indices (56) encoded into the data stream (14) for previous samples of the sequence of samples.

78. Apparatus of any of claims 57 to 77, wherein the number of reconstruction level sets (52) of the plurality (50) of reconstruction level sets (52) is two, and the apparatus is configured to

derive a subset index for each sample based on the selected set of reconstruction levels for the respective sample and a binary function of the quantization index for the respective sample, resulting in four possible values for the subset index; and

select (54), for the current sample (13’), the set (48) of reconstruction levels out of the plurality (50) of reconstruction level sets (52) depending on the subset indices for previous samples of the sequence of samples.

79. Apparatus of claims 78, wherein the apparatus is configured to

select (54), for the current sample (13’), the set (48) of reconstruction levels out of the plurality (50) of reconstruction level sets (52) using a selection rule which depends on the subset indices for a number of immediately preceding samples of the sequence of samples and to use the selection rule for all, or a portion, of the sequence of samples.

80. Apparatus of claim 79, wherein the number of immediately preceding samples of the sequence of samples on which the selection rule depends is two.

81. Apparatus of any of claims 78 to 80, wherein the subset index for each sample is derived based on the selected set of reconstruction levels for the sample and a parity of the quantization index for the sample.

82. Apparatus of any of claims 75 to 81 , wherein the selection rule for selecting a reconstruction level set out of a plurality of reconstruction level sets is realized via a state transition process, in such a way that

a state associated with the current sample (13’) uniquely determines the set (48) of reconstruction levels used for the current sample (13’),

and the state for the current sample depends on the state for an immediately preceding sample of the sequence of samples and a quantization index (58) encoded into the data stream for the immediately preceding sample of the sequence of samples.

83. Apparatus of claim 82, wherein the state for the current sample depends on the state for an immediately preceding sample of the sequence of samples and a binary function of the quantization index (58) encoded into the data stream for the immediately preceding sample of the sequence of samples.

84. Apparatus of claim 83, wherein the state for the current sample depends on the state for an immediately preceding sample of the sequence of samples and a parity of the quantization index (58) encoded into the data stream for the immediately preceding sample of the sequence of samples.

85. Apparatus of any of claims 82 to 84, wherein the number of possible states is four.

86. Apparatus of claim 85, wherein the number of reconstruction level sets (52) of the plurality (50) of reconstruction level sets (52) is two and the number of possible states is four (numbered from 0 to 3, inclusive), and the apparatus is configured to.

select a first reconstruction level set for a current sample if the state for the current sample is 0 or 1 ; and

select a second reconstruction level set for a current sample if the state for the current sample is 2 or 3.

87. Apparatus of claim 86, configured to perform a state transition in the state transition process by setting

the state for the current sample equal to 0, if the state for the preceding sample is equal to 0 and the parity of the preceding quantization index is equal to 0, or if the state for the preceding sample is equal to 1 and the parity of the preceding quantization index is equal to 1 ; and

the state for the current sample equal to 1 , if the state for the preceding sample is equal to 2 and the parity of the preceding quantization index is equal to 0, or if the state for the preceding sample is equal to 3 and the parity of the preceding quantization index is equal to 1 ; and

the state for the current sample equal to 2, if the state for the preceding sample is equal to 1 and the parity of the preceding quantization index is equal to 0, or if the state for the preceding sample is equal to 0 and the parity of the preceding quantization index is equal to 1 ; and

the state for the current sample equal to 3, if the state for the preceding sample is equal to 3 and the parity of the preceding quantization index is equal to 0, or if the state for the preceding sample is equal to 2 and the parity of the preceding quantization index is equal to 1.

88. Apparatus of any of claims 82 to 87, wherein a state for the first - in an order of the sequence of samples - sample of the sequence of samples is set equal to a predefined value.

89. Apparatus of claims 88, wherein the state for the first sample of the sequence of samples is set equal to zero.

90. Apparatus of any of claims 57 to 89, configured to

encode the quantization index (56) for the current sample (13') in form of

an absolute value which is indicative of the absolute of

a rank distance between a rank of zero and a rank of the one reconstruction level when ordering the selected set of quantization levels according to their values and, if zero is not included in the selected set of quantization levels, a rank distance between the rank of the one level and a rank of a smallest level of equal sign, when ordering the set of quantization levels according to their values, plus one, or

a rank distance between a rank of a predetermined level in the set of quantization levels which is of minimum absolute value, and a rank of the one reconstruction level when ordering the set of quantization levels according to their values, and

if the absolute value is greater than zero, a sign value which is indicative of the sign of the one reconstruction level.

91. Apparatus of any of claims 57 to 89, wherein

the number of reconstruction level sets (52) of the plurality (50) of reconstruction level sets (52) is two, and a first reconstruction level set of the plurality (50) of reconstruction level sets (52) comprises zero and even multiples of a predetermined quantization step size, and a second reconstruction level set of the plurality (50) of reconstruction level sets (52) comprises zero and odd multiples of the predetermined quantization step size, and

the apparatus configured to code the quantization index (56) for the current sample (13’) in form of an absolute value and, if the absolute value is greater than zero, a sign value which is indicative of the sign of the one reconstruction level, so that the reconstruction level is derivable from the absolute value and the sign value via a first function applied to the absolute value and the sign if the selected reconstruction level set is the first reconstruction level set and via a second function applied to the absolute value and the sign, if the selected reconstruction level set is the second reconstruction level set, with the first and second functions being symmetric with respect to the absolute value and reconstruction level.

92. Apparatus of any of claims 57 to 91 , configured to

decode an absolute value of the quantization index (56) for the current sample (13’) from the data stream using a binarization of the absolute value which comprises

a first bin which specifies whether the absolute value is greater than zero or not.

93. Apparatus of claim 92, wherein the binarization of the absolute value further comprises a second bin (100) which specifies whether the absolute value is greater than one or not, wherein the apparatus is configured to encode the second bin into the data stream only if the first bin indicates that the absolute value is greater than zero.

94. Apparatus of claims 92 or 93, wherein the binarization of the absolute value further comprises a further bin (100) which specifies a parity of the absolute value, wherein the apparatus is configured to encode the further bin into the data stream only if the first bin indicates that the absolute value is greater than zero

95. Apparatus of claim 93, wherein the binarization of the absolute value further comprises a further bin (100) which specifies a parity of the absolute value, wherein the apparatus is configured to encode the further bin into the data stream only if the second bin is included in the data stream and the second bin indicates that the absolute value is greater than one.

96. Apparatus of any of claims 57 to 95, configured to

encode an absolute value of the quantization index (56) for the current sample (13’) into the data stream using binary arithmetic encoding by

entropy encoding (85’) a first bin of a bin string onto which the absolute value is binarized, which first bin specifies whether the absolute value is greater than zero or not, using a first adaptive probability model, wherein the probability model is selected among a set of adaptive probability models and the selection depends on the set (48) of reconstruction values selected for the current sample (13’).

97. Apparatus of any of claims 82 to 95, configured to

encode an absolute value of the quantization index (56) for the current sample (13’) into the data stream using binary arithmetic encoding by

entropy encoding a first bin of a bin string onto which the absolute value is binarized, which first bin specifies whether the absolute value is greater than zero or not, using a first adaptive probability model, wherein the probability model is selected among a set of adaptive probability models and the selection depends on the state for the current sample.

98. Apparatus of claim 96 or 97, configured so that

the selection of the first adaptive probability model further depends on a parity of the quantization index for an immediately preceding sample of the sequence of samples.

99. Apparatus of any of claims 96 to 98, configured so that

the selection of the first adaptive probability model further depends on one or more preceding samples of the sequence of samples having a predetermined locational relationship to the current sample.

100. Apparatus of claim 99, wherein

the sequence of samples are transform coefficients of a transform coefficient block (10), and

the locational relationship to the current sample is determined by a template (122) positioned at the current sample (13’).

101. Apparatus of claim 100, wherein

the selection of the first adaptive probability model depends on

the sum of absolute quantization indices inside the template; and/or

the number of non-zero absolute quantization indices inside the template.

102. Apparatus of any of claims 57 to 101 , configured to

encode an absolute value of the quantization index (56) for the current sample (13’) into the data stream using binary arithmetic encoding by

entropy encoding (85') a second bin of the a string onto which the absolute value is binarized, which second bin specifies whether the absolute value is greater than one or not, using a second adaptive probability model, wherein the probability model is selected among a set of adaptive probability models and the selection depends on the set (48) of quantization values selected for the current sample (13’) or the state for the current sample.

103. Apparatus of claim 102, configured so that

the selection of the second adaptive probability model further depends on a parity of an immediately preceding sample of the sequence of samples.

104. Apparatus of any of claims 93 to 103, configured so that

the selection of the second adaptive probability model depends on one or more immediately preceding samples of the sequence of samples having a predetermined locational relationship to the current sample.

105. Apparatus of claim 104, wherein

the sequence of samples are transform coefficients of a transform coefficient block (10), and

the locational relationship to the current sample is determined by a template (122) positioned at the current sample (13’).

106. Apparatus of any of claims 92 to 105, wherein the apparatus is configured to

entropy encode the bins of the binarization of the absolute values of the quantization indexes in multiple passes over the scanning positions of the block or subblocks of the block.

107. Apparatus of claim 105, configured to perform a probability model selection for a currently encoded bin of the absolute value of the quantization index among a set of adaptive probability models depending on already encoded bins of the binarization of quantization indices of other samples of the sequence of samples.

108. Apparatus of any of claims 57 to 107, configured to

sequentially decode a further sequence of samples (13) which describe the media signal by use of mutually sample independent scalar dequantization.

109. Apparatus of any of claims 57 to 108, wherein the media signal is a video sequence of pictures and the samples of the sequence of samples are transform coefficients of a transform coefficient block (10) representing a transform of a predetermined transform block of transform bocks (284) the video pictures are partitioned into, wherein the apparatus switches between encoding the transform blocks by use of mutually sample independent scalar quantization and mutually sample dependent scalar quantization as applied for the predetermined transform block.

1 10. Apparatus of claim 109, configured to perform the switching between the encoding the transform blocks by use of mutually sample independent scalar quantization and mutually sample dependent scalar quantization depending on one or more coding parameters signaled in the data stream and varying across the picture or between pictures of the video sequence.

1 11. Apparatus of claim 109 or 1 10, configured to signal the switching between the encoding the transform blocks by use of mutually sample independent scalar quantization and mutually sample dependent scalar quantization by way of explicit signaling in the data stream.

1 12. Method for encoding a media signal into a data stream, comprising

sequentially encoding a sequence of samples (13) which describe the media signal by

selecting (54), for a current sample (13’), a set (48) of reconstruction levels out of a plurality (50) of reconstruction level sets (52) depending on quantization indices (58) encoded into the data stream (14) for previous samples of the sequence of samples,

quantizing (64) the current sample (13') onto one reconstruction level of the set (48) of reconstruction levels, and

encoding a quantization index (56) which indicates the one reconstruction level out of the selected set (48) of reconstruction levels for the current sample (13’) into the data stream (14).

1 13. Apparatus for decoding a media signal from a data stream, configured to

sequentially decode a sequence of samples (13) which describe the media signal by

selecting (54), for a current sample (13’), a set (48) of quantization levels out of a plurality (50) of quantization level sets (52) depending on quantization indices (58) decoded from the data stream (14) for previous samples of the sequence of samples,

decoding a quantization index (56) for the current sample (13’) from the data stream (14),

dequantizing (62) the current sample (13’) onto one level of the set (48) of quantization levels.

1 14. Apparatus of claim 113, wherein the media signal comprises a picture (212).

1 15. Apparatus of claim 113 or 1 14, wherein the media signal comprises a picture (212) and the sequence of samples is formed by transform coefficients of a transform coefficient block (10), scanned along a predetermined coefficient scan (14), wherein the apparatus is configured to

subject (36) the transform coefficient block (10) to an inverse transformation to obtain a transform block (284) of the picture (212)..

1 16. Apparatus of claim 1 15, configured to

predict the a picture content of the picture (212) within the transform block (284), wherein the transform block (284) represents a prediction residual of the prediction of the picture content of the picture within the transform block.

1 17. Apparatus of any of claims 1 13 to 1 16, configured to

select (54), for the current sample (13’), the set (48) of quantization levels out of the plurality (50) of quantization level sets (52) depending on a LSB portion of the quantization indices (58) decoded from the data stream (14) for previous samples of the sequence of samples.

118. Apparatus of any of claims 1 13 to 1 14, wherein the number of quantization level sets (52) of the plurality (50) of quantization level sets (52) is two.

119. Apparatus of any of claims 1 13 to 118, wherein the apparatus is configured to

select (54), for the current sample (13’), the set (48) of quantization levels out of the plurality (50) of quantization level sets (52) depending on a parity of the quantization indices (56) decoded from the data stream (14) for previous samples of the sequence of samples.

120. Apparatus of any of claims 113 to 1 19, wherein configured to

select (54), for the current sample (13’), the set (48) of quantization levels out of the plurality (50) of quantization level sets (52) using a selection rule which depends on the quantization indices (58) decoded from the data stream for a number of immediately preceding samples of the sequence of samples and to use the selection rule for all, or a portion, of the sequence of samples.

121. Apparatus of claim 120, wherein the number of immediately preceding samples of the sequence of samples on which the selection rule depends is two.

122. Apparatus of claim 120 or 121 , wherein the selection rule is realized via a state transition process, in such a way that a state associated with the current sample (13’) uniquely determines the set (48) of quantization levels used for the current sample (13') and depends on the state for an immediately preceding sample of the sequence of samples and a quantization index (58) decoded from the data stream for the immediately preceding sample of the sequence of samples.

123. Apparatus of claim 122, wherein a state for the first - in an order of the sequence of samples - sample of the sequence of samples is set equal to a predefined value.

124. Apparatus of claim 120 or 122, wherein the selection rule depends on the quantization index (58) decoded from the data stream for an immediately preceding sample of the sequence of samples and a set of quantization levels selected for the immediately preceding sample of the sequence of samples.

125. Apparatus of any of claims 1 13 to 124, configured to

parametrize the plurality (50) of quantization level sets (52) by way of a predetermined quantization step size and derive information on the predetermined quantization step size from the data stream (14).

126. Apparatus of any of claims 113 to 125, wherein the media signal comprises a picture (212) and the apparatus is configured to

Partition the picture (212) into transform blocks (284) and

derive each of a subset of the transform blocks (284) of the picture (212) by way of an inverse transformation of a transform coefficient block (10), wherein the transform coefficients of a predetermined transform coefficient block, scanned along a predetermined coefficient scan, form the sequence of samples, and

derive information on a predetermined quantization step size from the data stream (14) in a manner varying among subset of transform blocks, and

parametrize the plurality (50) of quantization level sets (52) by way of the predetermined quantization step size.

127. Apparatus of any of claims 113 to 126, wherein each of the plurality (50) of quantization level sets (52) consists of multiples of a predetermined quantization step size which is constant for the plurality (50) of quantization level sets (52).

128. Apparatus of any of claims 113 to 1 17, wherein the number of quantization level sets (52) of the plurality (50) of quantization level sets (52) is two and the plurality of quantization level sets comprises

a first quantization level set comprising zero and even multiples of a predetermined quantization step size, and

a second quantization level set comprising zero and odd multiples of the predetermined quantization step size.

129. Apparatus of any of claims 1 13 to 128, configured to

decode the quantization index (56) to the one level for the current sample (13’) in form of

an absolute value which is indicative of the absolute of

a rank distance between a rank of zero and a rank of the one level when ordering the set of quantization levels according to their values and, if zero is not included in the set of quantization levels, a rank distance between the rank of the one level and a rank of a smallest level of equal sign, when ordering the set of quantization levels according to their values, plus one, or

a rank distance between a rank of a predetermined level in the set of quantization levels which is of minimum absolute value, and a rank of the one level when ordering the set of quantization levels according to their values, and

if the absolute value is greater than zero, a sign value which is indicative of the sign of the one level.

130. Apparatus of any of claims 1 13 to129, configured to

decode an absolute value of the quantization index (56) to the one level for the current sample (13’) from the data stream using a binarization of the absolute value which comprises

a first bin which specifies whether the absolute value is greater than zero or not, and

131. Apparatus of claims 130, wherein the binarization of the absolute value further comprises a second bin (100) which specifies whether the absolute value is greater than one or not, where the second bin is only included in the data stream if the first bin indicates that the absolute values is greater than zero.

132. Apparatus of claims 130 or 131 , wherein the binarization of the absolute value further comprises a further bin (100) which specifies a parity of the absolute value, where the further bin is only included in the data stream if the first bin indicates that the absolute values is greater than zero

133. Apparatus of any of claims 1 13 to 132, configured to

decode an absolute value of the quantization index (56) to the one level for the current sample (13’) from the data stream using binary arithmetic decoding by

entropy decoding (85’) a first bin of a bin string onto which the absolute value is binarized, which first bin specifies whether the absolute value is greater than zero or not, using a first probability distribution estimation which depends on the set (48) of quantization values selected for the current sample (13’).

134. Apparatus of any of claims 1 13 to 132,

configured to select (54), for the current sample (13’), the set (48) of quantization levels out of the plurality (50) of quantization level sets (52) using a selection rule which depends on the quantization indices (58) decoded from the data stream for a number of immediately preceding samples of the sequence of samples and to use the selection rule for all, or a portion, of the sequence of samples,

wherein the number of immediately preceding samples of the sequence of samples on which the selection rule depends is two,

wherein the selection rule is realized via a state transition process, in such a way that a state associated with the current sample (13’) uniquely determines the set (48) of quantization levels used for the current sample (13') and depends on the state for an immediately preceding sample of the sequence of samples and a quantization index (58) decoded from the data stream for the immediately preceding sample of the sequence of samples,

wherein the apparatus is configured to decode an absolute value of the quantization index (56) to the one level for the current sample (13’) from the data stream using binary arithmetic decoding by

entropy decoding (85’) a first bin of a bin string onto which the absolute value is binarized, which first bin specifies whether the absolute value is greater than zero or not, using a first probability distribution estimation which depends on the state associated with the current sample (13’).

135. Apparatus of claim 134, configured so that

the first probability distribution estimation further depends on a parity of an immediately preceding sample of the sequence of samples.

136. Apparatus of claim 134 or 135, configured so that

the first probability distribution estimation further depends on one or more immediately preceding samples of the sequence of samples having a predetermined locational relationship to the current sample.

137. Apparatus of claiml 36, wherein

the sequence of samples are transform coefficients of a transform coefficient block (10), and

the locational relationship to the current sample is determined by a template (122) positioned at the current sample (13’).

138. Apparatus of any of claims 113 to 137, configured to

decode an absolute value of the quantization index (56) to the one level for the current sample (13’) from the data stream using binary arithmetic decoding by

entropy decode (85’) a second bin of the a string onto which the absolute value is binarized, which second bin specifies whether the absolute value is greater than one or not, using a second probability distribution estimation which depends on the set (48) of quantization values selected for the current sample (13’).

139. Apparatus of claim 138, configured so that

the second probability distribution estimation further depends on a parity of an immediately preceding sample of the sequence of samples.

140. Apparatus of claim 138 or 139, configured so that

the second probability distribution estimation further depends on one or more immediately preceding samples of the sequence of samples having a predetermined locational relationship to the current sample.

141. Apparatus of claim 140, wherein

the sequence of samples are transform coefficients of a transform coefficient block (10), and

the locational relationship to the current sample is determined by a template (122) positioned at the current sample (13’).

142. Apparatus of any of claims 113 to 141 , configured to

the sequence of samples are predetermined transform coefficients of a transform coefficient block, scanned along a predetermined coefficient scan (14),

derive a base quantization step size for the transform coefficient block from the data stream,

derive scale information from the data stream which defines as to how the base quantization step size is scaled in order to obtain quantization step sizes for the transform coefficients of the transform coefficient block (10) so that the quantization step sizes vary across the transform coefficient block (10),

parametrize the plurality (50) of quantization level sets (52) by a predetermined quantization step size obtained by scaling the base quantization step size according to the scale information.

143. Apparatus of any of claims 1 13 to142, configured to

sequentially decode a further sequence of samples (13) which describe the media signal by use of mutually sample independent scalar dequantization.

144. Apparatus of any of claims 1 13 to 143, wherein the media signal is a picture (212) and the samples of the sequence of samples are transform coefficients of a transform coefficient block (10) representing a transform of a predetermined transform block of transform bocks (284) the picture is partitioned into, wherein the apparatus switches between decoding the transform blocks by use of mutually sample independent scalar dequantization and mutually sample dependent scalar dequantizationa as applied for the predetermined transform block.

145. Apparatus of claim 144, configured to perform the switching between the decoding the transform blocks by use of mutually sample independent scalar dequantization and mutually sample dependent scalar dequantization depending on one or more coding parameters signaled in the data stream and varying across the picture.

146. Apparatus of claim 144 or 145, configured to derive the switching between the decoding the transform blocks by use of mutually sample independent scalar dequantization and mutually sample dependent scalar dequantization from explicit signaling in the data stream.

147. Method for decoding a media signal from a data stream, comprising

sequentially decoding a sequence of samples (13) which describe the media signal by

selecting (54), for a current sample (13’), a set (48) of quantization levels out of a plurality (50) of quantization level sets (52) depending on indices (58) decoded from the data stream (14) for previous samples of the sequence of samples,

decoding a quantization index (56) for the current sample (13’) from the data stream (14),

dequantizing (62) the current sample (13’) onto the one level of the set (48) of quantization levels.

148. Apparatus for encoding a media signal into a data stream, configured to

sequentially encode a sequence of samples (13) which describe the media signal by

selecting (54), for a current sample (13’), a set (48) of quantization levels out of a plurality (50) of quantization level sets (52) depending on quantization indices (58) encoded into the data stream (14) for previous samples of the sequence of samples,

quantizing (64) the current sample (13’) onto one level of the set (48) of quantization levels, and

encoding a quantization index (56) to the one level for the current sample (13’) into the data stream (14).

149. Apparatus of claim 148, wherein the media signal comprises a picture (212).

150. Apparatus of claim 148 or 149, wherein the media signal comprises a picture (212) and the apparatus is configured to

transform (46) a transform block (284) of the picture (212) to obtain a transform coefficient block (10), wherein predetermined transform coefficients of the transform coefficient block, scanned along a predetermined coefficient scan, form the sequence of samples.

151. Apparatus of claim 150, configured to

predict the a picture content of the picture (212) within the transform block (284), wherein the transform block (284) represents a prediction residual of the prediction of the picture content of the picture within the transform block.

152. Apparatus of any of claims 148 to 151 , configured to

select (54), for the current sample (13’), the set (48) of quantization levels out of the plurality (50) of quantization level sets (52) depending on a LSB portion of the quantization indices (58) encoded into the data stream (14) for previous samples of the sequence of samples.

153. Apparatus of any of claims 148 to 152, wherein the number of quantization level sets (52) of the plurality (50) of quantization level sets (52) is two.

154. Apparatus of any of claims 148 to 153, wherein the apparatus is configured to

select (54), for the current sample (13’), the set (48) of quantization levels out of the plurality (50) of quantization level sets (52) depending on a parity of the quantization indices (56) encoded into the data stream (14) for previous samples of the sequence of samples.

155. Apparatus of any of claims 148 to 154, wherein configured to

select (54), for the current sample (13’), the set (48) of quantization levels out of the plurality (50) of quantization level sets (52) using a selection rule which depends on the quantization indices (58) encoded into the data stream for a number of immediately preceding samples of the sequence of samples and to use the selection rule for all, or a portion, of the sequence of samples.

156. Apparatus of claim 155, wherein the number of immediately preceding samples of the sequence of samples on which the selection rule depends is two.

157. Apparatus of claim 155 or 156, wherein the selection rule is realized via a state transition process, in such a way that a state associated with the current sample (13’) uniquely determines the set (48) of quantization levels to be used for the current sample and depends on a state for an immediately preceding sample of the sequence of samples and the quantization index (58) encoded into the data stream for the immediately preceding sample of the sequence of samples.

158. Apparatus of claim 157, wherein a state for a first - in an order of the sequence - sample of the sequence of samples is set equal to a predefined value.

159. Apparatus of any of claims 155 to 158, wherein the selection rule depends on the quantization index (58) encoded into the data stream for an immediately preceding sample of the sequence of samples and a set of quantization levels selected for the immediately preceding sample of the sequence of samples.

160. Apparatus of any of claims 148 to 159, configured to

parametrize the plurality (50) of quantization level sets (52) by way of a predetermined quantization step size and signal information on the predetermined quantization step size in the data stream (14).

161. Apparatus of any of claims 148 to 160, wherein the media signal comprises a picture (212) and the apparatus is configured to

Partition the picture (212) into transform blocks (284) and

transform (46) each of a subset of the transform blocks (284) of the picture (212) to obtain a transform coefficient block (10), wherein predetermined transform coefficients of a predetermined transform coefficient block, scanned along a predetermined coefficient scan, form the sequence of samples, and

parametrize the plurality (50) of quantization level sets (52) by way of a predetermined quantization step size and signal information on the predetermined quantization step size in the data stream (14) in a manner varying among subset of transform blocks.

162. Apparatus of any of claims 148 to 160, wherein each of the plurality (50) of quantization level sets (52) consists of multiples of a predetermined quantization step size which is constant for the plurality (50) of quantization level sets (52).

163. Apparatus of any of claims 148 to 162, wherein the number of quantization level sets (52) of the plurality (50) of quantization level sets (52) is two and the plurality of quantization level sets comprises

a first quantization level set comprising zero and even multiples of a predetermined quantization step size, and

a second quantization level set comprising zero and odd multiples of the predetermined quantization step size.

164. Apparatus of any of claims 148 to 163, configured to

encode the quantization index (56) to the one level for the current sample (13’) as

an absolute value which is indicative of the absolute of

a rank distance between a rank of zero and a rank of the one level when ordering the set of quantization levels according to their values and, if zero is not included in the set of quantization levels, a rank distance between the rank of the one level and a rank of a smallest level of equal sign, when ordering the set of quantization levels according to their values, plus one, or

a rank distance between a rank of a predetermined level in the set of quantization levels which is of minimum absolute value, and a rank of the

one level when ordering the set of quantization levels according to their values, and

if the absolute value is greater than zero, a sign value which is indicative of the sign of the one level.

165. Apparatus of any of claims 148 to 164, configured to

encode an absolute value of the quantization index (56) to the one level for the current sample (13’) using a binarization of the absolute value which comprises

a first bin which specifies whether the absolute value is greater than zero or not, and

166. Apparatus of claims 165, wherein the binarization of the absolute value further comprises a second bin (100) which specifies whether the absolute values is greater than one or not, where the second bin is only included in the data stream if the first bin indicates that the absolute value is greater than zero.

167. Apparatus of claims 163 or 166, wherein the binarization of the absolute value further comprises a further bin (100) which specifies a parity of the absolute value, where the further bin is only included in the data stream if the first bin indicates that the absolute values is greater than zero.

168. Apparatus of any of claims 148 to 167, configured to

encode an absolute value of the quantization index (56) to the one level for the current sample (13’) using binary arithmetic coding by

binarizing (80) the absolute value to obtain a bin string (82), and

entropy code (85) a first bin of the bin string, which specifies whether the absolute value is greater than zero or not, using a first probability distribution estimation which depends on the set (48) of quantization values selected for the current sample (13').

169. Apparatus of claim 168,

configured to select (54), for the current sample (13’), the set (48) of quantization levels out of the plurality (50) of quantization level sets (52) using a selection rule which depends on the quantization indices (58) encoded into the data stream for a number of immediately preceding samples of the sequence of samples and to use the selection rule for all, or a portion, of the sequence of samples,

wherein the number of immediately preceding samples of the sequence of samples on which the selection rule depends is two,

wherein the selection rule is realized via a state transition process, in such a way that a state associated with the current sample (13’) uniquely determines the set (48) of quantization levels used for the current sample (13’) and depends on the state for an immediately preceding sample of the sequence of samples and a quantization index (58) encoded into the data stream for the immediately preceding sample of the sequence of samples,

wherein the apparatus is configured to encode an absolute value of the quantization index (56) to the one level for the current sample (13’) into the data stream using binary arithmetic encoding by

entropy encoding (85’) a first bin of a bin string onto which the absolute value is binarized, which first bin specifies whether the absolute value is greater than zero or not, using a first probability distribution estimation which depends on the state associated with the current sample (13’).

170. Apparatus of claim 169, configured so that

the first probability distribution estimation further depends on a parity of an immediately preceding sample of the sequence of samples.

171. Apparatus of claim 168 or 170, configured so that

the first probability distribution estimation further depends on one or more immediately preceding samples of the sequence of samples having a predetermined locational relationship to the current sample.

172. Apparatus of claim 171 , wherein

the sequence of samples are transform coefficients of a transform coefficient block (10), and

the locational relationship to the current sample is determined by a template (122) positioned at the current sample (13’).

173. Apparatus of any of claims 148 to 172, configured to

encode an absolute value of the quantization index (56) to the one level for the current sample (13’) using binary arithmetic coding by

binarizing (80) the absolute value to obtain a bin string (82), and

entropy code (85) a second bin of the bin string, which specifies whether the absolute value is greater than one or not, using a second probability distribution estimation which depends on the set (48) of quantization values selected for the current sample (13’).

174. Apparatus of claim 173, configured so that

the second probability distribution estimation further depends on a parity of an immediately preceding sample of the sequence of samples.

175. Apparatus of claim 173 or 174, configured so that

the second probability distribution estimation further depends on one or more immediately preceding samples of the sequence of samples having a predetermined locational relationship to the current sample.

176. Apparatus of claim 175, wherein

the sequence of samples are transform coefficients of a transform coefficient block (10), and

the locational relationship to the current sample is determined by a template (122) positioned at the current sample (13’).

177. Apparatus of any of claims 148 to 176, configured to

the sequence of samples are predetermined transform coefficients of a transform coefficient block, scanned along a predetermined coefficient scan (14),

signal a base quantization step size for the transform coefficient block in the data stream,

signal scale information in the data stream which defines as to how the base quantization step size is scaled in order to obtain quantization step sizes for the transform coefficients of the transform coefficient block (10) so that the quantization step sizes vary across the transform coefficient block (10),

parametrize the plurality (50) of quantization level sets (52) by a predetermined quantization step size obtained by scaling the base quantization step size according to the scale information.

178. Apparatus of any of claims 148 to 177, configured to

sequentially encode a further sequence of samples (13) which describe the media signal by use of mutually sample independent scalar quantization.

179. Apparatus of any of claims 148 to 178, wherein the media signal is a picture (212) and the samples of the sequence of samples are transform coefficients of a transform coefficient block (10) representing a transform of a predetermined transform block of transform bocks (284) the picture is partitioned into, wherein the apparatus switches between coding the transform blocks by use of mutually sample independent scalar quantization and mutually sample dependent scalar quantization as applied for the predetermined transform block.

180. Apparatus of claim 179, configured to perform the switching between the coding the transform blocks by use of mutually sample independent scalar quantization and mutually sample dependent scalar quantization depending on one or more coding parameters signaled in the data stream and varying across the picture.

181 . Apparatus of claim 179 or 180, configured to signaling the switching between the coding the transform blocks by use of mutually sample independent scalar quantization and mutually sample dependent scalar quantization by explicit signaling.

182. Method for encoding a media signal into a data stream, comprising

sequentially encoding a sequence of samples (13) which describe the media signal by

selecting (54), for a current sample (13'), a set (48) of quantization levels out of a plurality (50) of quantization level sets (52) depending on quantization indices (58) encoded into the data stream (14) for previous samples of the sequence of samples,

quantizing (64) the current sample (13’) onto one level of the set (48) of quantization levels, and

encoding a quantization index (56) to the one level for the current sample

(13’) into the data stream (14).

183. Computer program having a program code for performing, when running on a computer, a method according to claim 147, 112, 182 or 56.

184. Data stream generated using a method according to claim 1 12 or 182.