1. A method for providing formatted information (15), in a standard format, to image-processing means (PI), especially to software and/or components; the said formatted information (15) being related to the defects (P5) of an appliance chain (P3); the said appliance chain (P3) comprising in particular at least one image-capture appliance (1) and/or one image-restitution appliance (19); the said image-processing means (PI) using the said formatted information (15) to modify the quality of at least one image (103) derived from or addressed to the said appliance chain (P3) ; the said formatted information (15) comprising : data characterizing the defects (P5) of the said image-capture appliance (1) especially the distortion characteristics ; and/or data characterizing the defects (P5) of the said image-restitution appliance, especially the distortion characteristics ; the defects being comprised in the group comprising : geometric defects, sharpness defects colorimetry defects, geometric distortion defects, geometric chromatic abberation defects, geometric vignetting defects, contrast defects, colorimetry defects, in particular rendering of colors and color cast, defects of flash uniformity, sensor noise, grain, astigmatism defects and spherical abberation defects, the said method comprising the stage of filling in at least one field (91) of the said standard format with the said formatted information (15); the said field (91) being designated by a field name; and said field (91) containing at least one field value.

2. A "method according to claim 1, the said method being such that the said field (91) is related to the sharpness defects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19); the said method being such that the said field (91) contains at least one value related to the sharpness defects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19).

3. A method according to any one of claims 1 or 2; the said method being such that the said field (91) is related to the colorimetry defects (P5) of the said image -capture appliance (1) and/or of the said image-restitution appliance (19); the said method being such that the said field (91) contains at least one value related to the colorimetry defects (P5) of the said image - capture appliance (1) and/or of the said image -restitution appliance (19).

4. A method according to any one of claims 1 to 3; the said method being such that the said field (91) is related to the geometric distortion defects (P5) and/or to the geometric chromatic aberration defects (P5) of the said image -capture appliance (1) and/or of the said image -restitution appliance (19); the said method being such that the said field (91) contains at least one value related to the geometric distortion defects (P5) and/or to the geometric chromatic aberration defects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19).

5. A method according to any one of claims 1 to 4; the said method being such that the said field (91) is related to the geometric vignetting defects (P5) and/or to the contrast defects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19); the said method being such that the said field (91) contains at least one value related to the geometric vignetting defects (P5) and/or to the contrast defects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19).

6. A method according to any one of claims 1 to 5; the said method being such that the said field (91) contains at least one value related to the deviations (14).

7. A method according to claim 2; the said formatted information (15) being composed at least partly of parameters (P9) of a parameterizable transformation model (12) representative of the sharpness defects (P5) of the image -capture appliance (1) and/or of the image -restitution appliance (19); the said method being such that the said value or values contained in the said field (91) related to the sharpness defects (P5) are composed at least partly of parameters (P9) of the said parameterizable transformation model (12); in such a way that the said image -processing means (P1) can use the said parameters (P9) of the said parameterizable transformation model (12) to calculate the corrected shape or the corrected restitution shape of a point of the image (103).

8. A method according to claim 3; the said formatted information (15) being composed at least partly of parameters (P9) of a parameterizable transformation model (12) representative of the colorimetry defects (P5) of the image - capture appliance (1) and/or of the image -restitution appliance (19); the said method being such that the said value or values contained in the said field (91) related to the colorimetry defects (P5) are composed at least partly of parameters (P9) of the said parameterizable transformation model (12); in such a way that the said image -processing means (P1) can use the said parameters (P9) of the said parameterizable transformation model (12) to calculate the corrected color or the corrected restitution color of a point of the image (103) .

9. A method according to claim 4; the said formatted information (15) being composed at least partly of parameters (P9) of a parameterizable transformation model (12) representative of the geometric distortion defects (P5) and/or of the geometric chromatic aberration d efects (P5) of the image - capture appliance (1) and/or of the image -restitution appliance (19); the said method being such that the said value or values contained in the said field (91) related to the geometric distortion defects (P5) and/or to the geometric chromatic aberration defects (P5) are composed at least partly of parameters (P9) of the said parameterizable transformation model (12); in such a way that the said image -processing means (P1) can use the said parameters (P9) of the said parameterizable transformation model (12) to calculate the corrected position or the corrected restitution position of a point of the image (103).

10. A method according to claim 5; the said formatted information (15) being composed at least partly of parameters (P9) of a parameterizable transformation model (12) representative of the geometric vignetting defects (P5) and/or of the contrast defects (P5) of the image -capture appliance (1) and/or of the image-restitution appliance (19); the said method being such that the said value or values contained in the said field (91) related to the geometric vignetting defects (P5) and/or to the contrast defects (P5) are composed at least partly of parameters (P9) of the said parameterizable transformation model (12) ; in such a way that the said image-processing means (P1) can use the said parameters (P9) of the said parameterizable transformation model (12) to calculate the corrected intensity or the corrected restitution intensity of a point of the image (103) .

11. A method according to any one of claims 1 to 10; to provide the said formatted information (15) in a standard format to the said image-processing means (P1), the said method additionally includes the stage of associating the said formatted information (15) with the said image (103) .

12. A method according to claim 11; the said image (103) being transmitted in the form of a file (P100) ; the said file (P100) additionally containing the said formatted information (15) .

13. A method according to any one of claims 1 to 12; the said image-capture appliance (1) and/or the said image - restitution appliance (19) including at least one variable characteristic (P6) depending on the image (103), especially the focal length; at least one of the said defects (P5), especially the geometric distortion defect of said image-capture appliance (1) and/or of the said image- restitution appliance (19) depending on the said variable characteristic (P6); the said method being such that at least one of the said fields (91) contains at least one value that is a function of the said variable characteristic (P6) depending on the image (103); . in such a way that the image-processing means (P1) can process the said image (103) as a function of the said variable characteristics (P6).

14. A method according to any one of claims 1 to 13, the said formatted information (15) being measured formatted information (P101), at least in part.

15. A method according to any one of claims 1 to 14, the said formatted information (15) being extended formatted information (PI02), at least in part.

16. A method according to any one of claims 1 to 15, the said image (103) being composed of color planes (P20); the said formatted information (15) being at least partly related to the said color planes (P20).

17. A system for providing formatted information (15), in a standard format to image-processing means (P1), especially to software and/or components; the said formatted information (15) being related to the defects (P5) of an appliance chain (P3); the said appliance chain (P3) comprising in particular at least one image-capture appliance (1) and/or one image - restitution appliance (19); the said image-processing means (P1) using the said formatted information (15) to modify the quality of at least one image 103 derived from or addressed to the said appliance chain (P3) ; the system comprising - means for producing formatted information, the said formatted information (15) comprising : data characterizing the defects (P5) of the said image-capture appliance (1) especially the distortion characteristics; and/or data characterizing the defects (P5) of the said image-restitution appliance (19), especially the distortion characteristics; data-processing means for filing in at least one field (91) of the said standard format with the said formatted information (15); the said field (91) being designated by a field name; the said field (91) containing at least one field value, and - means for transmitting formatted information to the image-processing means.

18. A system according to claim 17, the said system being such that the said field (91) is related to the sharpness defects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19); the said system being such that the said field (91) contains at least one value related to the sharpness defects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19).

19. A system according to any one of claims 17 or 18; the said system being such that the said field (91) is related to the colorimetry defects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19); the said system being such that the said field (91) contains at least one value related to the colorimetry defects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19).

20. A system according to any one of claims 17 to 19; the said system being such that the said field (91) is related to the geometric distortion defects (P5) and/or to the geometric chromatic abberation defects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19); the said system being such that the said field (91) contains at least one value related to the geometric distortion defects (P5) and/or to the geometric chromatic aberration defects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19) .

21. A system according to any one of claims 17 to 20; the said system being such that the said field (91) is related to the geometric vignetting defects (P5) and/or to the contrast de fects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19); the said system being such that the said field (91) contains at least one value related to the geometric vignetting defects (P5) and/or to the contrast defects (P5) of the said image-capture appliance (1) and/or of the said image-restitution appliance (19) .

22. A system according to any one of claims 17 to 21; the said system being such that the said field (91) contains at least one value related to the deviations (14).

23. A system according to claim 18; the said formatted information (15) being composed at least partly of parameters (P9) of a parameterizable transformation model (12) representative of the sharpness defects (P5) of the image -capture appliance (1) and/or of the image -restitution appliance (19); the said system being such that the said value or values contained in the said field (91) related to the sharpness defects (P5) are composed at least partly of parameters (P9) of the said parameterizable transformation model (12).

24. A system according to claim 19; the said formatted information (15) being composed at least partly of parameters (P9) of a parameterizable transformation model (12) representative of the colorimetry defects (P5) of th e image- capture appliance (1) and/or of the image -restitution appliance (19) ; the said system being such that the said value or values contained in the said field (91) related to the colorimetry defects (P5) are composed at least partly of parameters (P9) of the said parameterizable transformation model (12).

25. A system according to claim 20; the said formatted information (15) being composed at least partly of parameters (P9) of a parameterizable transformation model (12) representative of the geometric distortion defects (P5) and/or of the geometric chromatic aberration defects (P5) of the image - capture appliance (1) and/or of the image -restitution appliance (19) ; the said system being such that the said value or values contained in the said field (91) related to the geometric distortion defects (P5) and/or to the geometric chromatic aberration defects (P5) are composed at least partly of parameters (P9) of the said parameterizable transformation model (12) .

26. A system according to claim 21; the said formatted information (15) being composed at least partly of parameters (P9) of a parameterizable transformation model (12) representative of the geometric vignetting defects (P5) and/or of the contrast defects (P5) of the image -capture appliance (1) and/or of the image-restitution appliance (19); the said system being such that the said value or values contained in the said field (91) related to the geometric vignetting defects (P5) and/or to the contrast defects (P5) are composed at least partly of parameters (P9) of the said parameterizable transformation model (12).

27. A system according to any one of claims 17 to 26; to provide the said formatted information (15) in a standard format to the said image-processing means (P1), the said system additionally includes data-processing means for associating the said formatted information (15) with the said image (103).

28. A system according to claim 27; the said system including transmission means to transmit the said image (103) in the form of a file (P100) ; the said file (P100) additionally containing the said formatted information (15).

29. A system according to any one of claims 17 to 28; the said image-capture appliance (1) and/or the said image - restitution appliance (19) including at least one variable characteristic (P6) depending on the image (103), especially the focal length; at least one of the said defects (P5), especially the geometric distortion defect of the said image -capture appliance (1) and/or of the said image -restitution appliance (19) depending on the said variable characteristic (P6) ; the said system being such that at least one of the said fields (91) contains at least one value that is a function of the said variable characteristic (P6) depending on the image (103).

30. A system according to any one of claims 17 to 29; the said formatted information (15) being measured formatted information (P101), at least in part.

31. A system according to any one of claims 17 to 30; the said formatted information (15) being extended formatted information (P102), at least in part.

32. A system according to any one of claims 17 to 31; the said image (103) being composed of color planes (P20); the said formatted information (15) being at least partly related to the said color planes (P20). The invention concerns a method for providing, in accordance with a standard format, formatted data (16) to image processing means (P1), in particular software and/or components. The formatted date (15) are related to the defects (P5) of a set of appliances (P3) including an image capture appliance and/or an image scanning appliance. The image processing means (P) use the formatted data (15) to modify the quality of at least one image (103) derived from or addressed to said chain of appliances (P3). The formatted data (15) comprises;data characterising the defects (P5) of said image capture appliance, in particular the distortion characteristics; and/or data characterising the defects (P5) of said image scanning appliance, in particular the distortion characteristics. The method comprises a step which consists in informing at least a field of said standard format with the formatted data (15). Said field is given a field name. Said field contains at least a field value.

METHOD AND SYSTEM FOR PROVIDING FORMATTED INFORMATION
IN STANDARD FORMAT TO IMAGE-PROCESSING MEANS
Preamble of the description
Field in question, problem posed
The present invention relates to a method and a system for providing formatted
information in a standard format to image-processing means.
Normal operation of image sensing and image reproduction, and classical image
correction operations are described in the following patent applications : US 5,
353 392 of 10.04.1991 in the name of Luquet et al EP 0 867 690 of 25.03.1998
in the name of Nippon Telegraph and Telephone Corporation, and EP 0 964
353 of 08.06.1999 in the name of Canon Kabushiki Kaisha.
Solution
Method
The invention relates to a method for providing formatted information in a
standard format to image-processing means, especially software and/or
components. The formatted information is related to the defects of an appliance
chain. The appliance chain includes in particular at least one image-capture
appliance and/or one image-restitution appliance. The image-processing
means use the formatted information to modify the quality of at least one image
derived from or addressed to the said appliance chain. The formatted
information includes data characterizing the defects of the image-capture
appliance, especially the distortion characteristics, and/or data
characterizing the defects of the
image-restitution appliance, especially the distortion
characteristics.
The method includes the stage of filling in at least one
field of the said standard format with the formatted information.
The field is designated by a field name. The field contains at
least one field value.
Preferably, according to the invention, the method is such
that the field is related to the sharpness defects of the image -
capture appliance and/or of the image -restitution appliance. The
method is such that the field contains at least one value related
to the sharpness defects of the image -capture appliance and/or of
the image-restitution appliance.
Preferably, according to the invention, the method is such
that the field is related to the colorimetry defects of the
image-capture appliance and/or of the image -restitution
appliance. The method is such that the field contains at least
one value related to the colorimetry defects of the image -capture
appliance and/or of the image -restitution appliance.
Preferably, according to the invention, the method is such
that the field is related to the geometric distortion defects
and/or to the geometric chromatic aberration defects of the
image-capture appliance and/or of the image -restitution
appliance. The method is such that the field contains at least
one value related to the geometric distortion defects and/or to
the geometric chromatic aberration defects of the image -capture
appliance and/or of the image -restitution appliance.
Preferably, according to the invention, the method is such
that the field is related to the geometric vignetting defects
and/or to the contrast defects of the image -capture appliance
and/or of the image-restitution appliance. The method is such
that the field contains at least one value related to the
geometric vignetting defects and/or to the contrast defects of
the image-capture appliance and/or of the image -restitution
appliance.
Preferably, according to the invention, the method is such
that the field cont ains at least one value related to the
deviations.
Preferably, according to the invention, the formatted
information is composed at least partly of parameters of a
parameterizable transformation model representative of the
sharpness defects of the image -capture appliance and/or of the
restitution appliance. The method is such that the value or
values contained in the field related to the sharpness defects
are composed at least partly of parameters of the parameterizable
transformation model. It results from the combination of
technical features that the image -processing means can use the
parameters of the parameterizable transformation model to
calculate the corrected shape or the corrected restitution shape
of an image point.
Preferably, according to the invention, the formatted
information is composed at least partly of parameters of a
parameterizable transformation model representative of the
colorimetry defects of the image -capture appliance and/or of the
restitution appliance. The method is such that t he value or
values contained in the field related to the colorimetry defects
are composed at least partly of parameters of the parameterizable
transformation model. It results from the combination of
technical features that the image -processing means can u se the
parameters of the parameterizable transformation model to
calculate the corrected color or the corrected restitution color
of an image point.
Preferably, according to the invention, the formatted
information is composed at least partly of parameter s of a
parameterizable transformation model representative of the
geometric distortion defects and/or of the geometric chromatic
aberration defects of the image -capture appliance and/or of the
restitution appliance. The method is such that the value or
values contained in the field related to the geometric distortion
defects and/or to the geometric chromatic aberration defects are
composed at least partly of parameters of the pararaeterizable
transformation model. It results from the combination of
technical features that the image-processing means can use the
parameters of the parameterizable transformation model to
calculate the corrected position or the corrected restitution
position of an image point.
Preferably, according to the invention , the formatted
information is composed at least partly of parameters of a
parameterizable transformation model representative of the
geometric vignetting defects and/or of the contrast defects of
the image-capture appliance and/or of the restitution appliance.
The method is such that the value or values contained in the
field related to the geometric vignetting defects and/or to the
contrast defects are composed at least partly of parameters of
the parameterizable transformation model. It results from the
combination of technical features that the image -processing means
can use the parameters of the parameterizable transformation
model to calculate the corrected intensity or the corrected
restitution intensity of an image point.
Association of formatted information with the image
Preferably, according to the invention , in order to provide
formatted information in a standard format to image -processing
means, the method additionally includes the stage of associating
the formatted information with the image.
Preferably, according to the invention, the image is
transmitted in the form of a file. The file additionally contains
the formatted information.
Variable focal length
Preferably, according to the invention , the image-capture
appliance and/or the image-restitution appliance includes at
least one variable characteristic depending on the image,
especially the focal length. At least one of the defects of the
image-capture appliance and/or of the image-restitution
appliance, especially the geometric distortion defect, dep ends on
the variable characteristic. The method is such that at least one
of the fields contains at least one value that is a function of
the variable characteristic depending on the image. It results
from the combination of technical features that the ima ge-
processing means can process the image as a function of the
variable characteristics.
Measured formatted information
Preferably, according to the invention , the formatted
information is measured formatted information, at least in part.
Thus, in the case of this alternative embodiment, the defects are
small.
Preferably, according to the invention , the formatted
information is extended formatted information, at least in part.
Thus, in the case of this alternative embodiment, the formatted
information occupies little memory. Thus also, the image -
processing calculations are faster.
The image can be composed of color planes. Preferably in
the case of this alternative embodiment according to the
invention, the formatted information is at least partly relat ed
to the color planes. It results from the combination of technical
features that the processing of the image can be separated into
processing operations related to each color plane. It results
from the combination of technical features that, by decomposi ng
the image into color planes before processing, it is possible to
arrive at positive pixel values in the color planes.
System
The invention relates to a system for providing formatted
information in a standard format to image -processing means,
especially software and/or components. The formatted information
is related to the defects of an appliance chain. The appliance
chain includes in particular at least one image -capture appliance
and/or one image restitution -appliance. The image-processing
means use the formatted information to modify the quality of at
least one image derived from or addressed to the appliance chain.
The formatted information includes data characterizing the
defects of the image -capture appliance, especially the distortion
characteristics, and/or data characterizing the defects of the
image-restitution appliance, especially the distortion
characteristics.
The system includes data -processing means for filling in at
least one field of the standard format with the formatted
information. The field is designated by a field name. The field
contains at least one field value.
Preferably, according to the invention , the system is such
that the field is related to the sharpness defects of the image -
capture appliance and/or of the image -restitution appliance. The
system is such that the field contains at least one value related
to the sharpness defects of the image -capture appliance and/or of
the image-restitution appliance.
Preferably, according to the invention , the system is such
that the field is related to the colorimetry defects of the
image-capture appliance and/or of the image -restitution
appliance. The system is such that the field contains at least
one value related to the colorimetry defects of the image -capture
appliance and/or of the image-restitution appliance.
Preferably, according to the invention , the system is such
that the field is related to the geometric distortion defects
and/or to the geometric chromatic aberration defects of the
image-capture appliance and/or of the imag e-restitution
appliance. The system is such that the field contains at least
one value related to the geometric distortion defects and/or to
the geometric chromatic aberration defects of the image -capture
appliance and/or of the image -restitution appliance .
Preferably, according to the invention , the system is such
that the field is related to the geometric vignetting defects
and/or to the contrast defects of the image -capture appliance
and/or of the image-restitution appliance. The system is such
that the field contains at least one value related to the
geometric vignetting defects and/or to the contrast defects of
the image-capture appliance and/or of the image -restitution
appliance.
Preferably, according to the invention , the system is such
that the field contains at least one value related to the
deviations.
Preferably, according to the invention , the formatted
information is composed at least partly of parameters of a
parameterizable transformation model representative of the
sharpness defects of the image-capture appliance and/or of the
restitution appliance. The system is such that the value or
values contained in the field related to the sharpness defects
are composed at least partly of parameters of the parameterizable
transformation model.
Preferably, according to the invention , the formatted
information is composed at least partly of parameters of a
parameterizable transformation model representative of the
colorimetry defects of the image -capture appliance and/or of the
restitution appliance. The system is such that the value or
values contained in the field related to the colorimetry defects
are composed at least partly of parameters of the parameterizable
transformation model.
Preferably, according to the invention , the formatted
information i s composed at least partly of parameters of a
parameterizable transformation model representative of the
geometric distortion defects and/or of the geometric chromatic
aberration defects of the image -capture appliance and/or of the
restitution appliance. The system is such that the value or
values contained in the field related to the geometric distortion
defects and/or to the geometric chromatic aberration defects are
composed at least partly of parameters of the parameterizable
transformation model.
Preferably, according to the invention , the formatted
information is composed at least partly of parameters of a
parameterizable transformation model representative of the
geometric vignetting defects and/or of the contrast defects of
the image-capture appliance and/or of the restitution appliance.
The system is such that the value or values contained in the
field related to the geometric vignetting defects and/or to the
contrast defects are composed at least partly of parameters of
the parameterizable transformation model.
Association of formatted information with the image
Preferably, according to the invention , in order to provide
formatted information in a standard format to image -processing
means, the system additionally includes data -processing means for
associating the formatted information with the image.
Preferably, according to the invention , the system includes
transmission means for transmitting the image in the form of a
file. The file additionally contains the formatted information.
Variable focal length
The image-capture appliance and/or the image -restitution
appliance can include at least one variable characteristic
depending on the image, especially the focal length. At least one
of the defects of the image -capture appliance and/or of the
image-restitution appliance, especially the geometric distortion
defect, depends on the variable characteristic. Preferably in the
case of this alternative embodiment according to the invention,
the system is such that at least one of the fields contains at
least one value that is a function of the variable characteristic
depending on the image.
Alternative versions of formatted information
Preferably, according to the invention , the formatted
information is measured formatted information, at least in par t.
Preferably, according to the invention , the formatted
information is extended formatted information, at least in part.
The image can be composed of color planes. Preferably in
the case of this alternative embodiment according to the
invention, the formatted information is at least partly related
to the color planes.
Detailed description
Other characteristics and advantages of the invention will
become apparent upon reading of the description of alternative
embodiments of the invention, provided by w ay of indicative and
non-limitative examples, and of the i figures, wherein
respectively:
- Fig. 1 illustrates a schematic view of image capture,
- Fig. 2 illustrates a schematic view of image restitution,
- Fig. 3 illustrates a schematic view of the pixe is of an
image,
- Figs. 4a and 4b illustrate two schematic views of a
reference scene,
- Fig. 5 illustrates the organizational diagram of the
method with which the difference between the mathematical image
and the corrected image can be calculated,
- F ig. 6 illustrates the organizational diagram of the
method with which the best restitution transformation for an
image-restitution means can be obtained,
- Fig. 7 illustrates a schematic view of the elements
composing the system according to the invention ,
Fig. 8 illustrates a schematic view of fields of
formatted information,
Fig. 9a illustrates a schematic front view of a
mathematical point,
- Fig. 9b illustrates a schematic front view of a real
point of an image,
Fig. 9c illustrates a schemati c side view of a
mathematical point,
- Fig. 9d illustrates a schematic profile view of a real
point of an image,
- Fig. 10 illustrates a schematic view of an array of
characteristic points,
- Fig. 11 illustrates the organizational diagram of the
method with which the formatted information can be obtained,
- Fig. 12 illustrates the organizational diagram of the
method with which the best transformation for an image -capture
appliance can be obtained,
- Fig. 13 illustrates the organizational diagram of th e
method with which the quality of an image derived from or
addressed to a chain of appliances can be modified,
Fig. 14 illustrates an example of a file containing
formatted information,
- Fig. 15 illustrates an example of formatted information,
- Fig . 16 illustrates a representation of parameters of
parameterizable models,
- Fig. 17 illustrates an organizational diagram of the
method with which the best transformation for an image -
restitution appliance can be obtained.
Fig. 1 illustrates a scene 3 c ontaining an object 107, a
sensor 101 and sensor surface 110, an optical center 111, an
observation point 105 on a sensor surface 110, an observation
direction 106 passing through observation point 105, optical
center 111, scene 3, and a surface 10 geometr ically associated
with sensor surface 110.
Fig. 2 illustrates an image 103, an image -restitution means
19 and a restituted image 191 obtained on the restitution medium
190.
Fig. 3 illustrates a scene 3, an image -capture appliance 1
and an image 103 composed of pixels 104.
Figs. 4a and 4b illustrate two alternative versions of a
reference scene 9.
Fig. 5 illustrates an organizational diagram employing a
scene 3, a mathematical projection 8 giving a mathematical image
70 of scene 3, a real projection 72 g iving an image 103 of scene
3 for the characteristics 74 used, a parameterizable
transformation model 12 giving a corrected image 71 of image 103,
the corrected image 71 exhibiting a difference 73 compared with
mathematical image 70.
Fig. 6 illustrates an organizational diagram employing an
image 103, a real restitution projection 90 giving a restituted
image 191 of image 103 for the restitution characteristics 95
used, a parameterizable restitution transformation model 97
giving a corrected restitution im age 94 of image 103, a
mathematical restitution projection 96 giving a mathematical
restitution image 92 of corrected restitution image 94 and
exhibiting a restitution difference 93 compared with restituted
image 191.
Fig. 7 illustrates a system comprisin g an image-capture
appliance 1 composed of an optical system 100, of a sensor 101
and of an electronic unit 102. Fig. 7 also illustrates a memory
zone 16 containing an image 103, a database 22 containing
formatted information 15, and means 18 for transmiss ion of
completed image 120 composed of image 103 and formatted
information 15 to calculating means 17 containing image -
processing software 4 .
Fig. 8 illustrates formatted information 15 composed of
fields 91.
Figs. 9a to 9d illustrate a mathematical imag e 70, an image
103, the mathematical position 40 of a point, and the
mathematical shape 41 of a point, compared with the real position
50 and the real shape 51 of the corresponding point of the image.
Fig. 10 illustrates an array 80 of characteristic poin ts.
Fig. 11 illustrates an organizational diagram employing an
image 103, the characteristics 74 used, and a database 22 of
characteristics. The formatted information 15 is obtained from
the characteristics 74 used and stored in database 22. The
completed image 120 is obtained from image 103 and formatted
information 15.
Fig. 12 illustrates an organizational diagram employing a
reference scene 9, a mathematical projection 8 giving a synthetic
image class 7 of reference scene 9, and a real projection 72
giving a reference image 11 of reference scene 9 for the
characteristics 74 used. This organizational diagram also employs
a parameterizable transformation model 12 giving a transformed
image 13 of reference image 11. Transformed image 13 exhibits a
deviation 14 compared with synthetic image class 7.
Fig. 17 illustrates an organizational diagram employing a
restitution reference 209, a real restitution projection 90
giving a restituted reference 211 of the said restitution
reference 209 for the restitution c haracteristics 95 used, a
parameterizable restitution transformation model 97 giving a
corrected reference restitution image 213 of the said restitution
reference 209, a parameterizable reverse restitution
transformation model 297 producing the said restit ution reference
209 from the said corrected reference restitution image 213. This
organizational diagram also employs a mathematical restitution
projection 96 giving a synthetic restitution image 307 of the
corrected reference restitution image 213. The sa id synthetic
restitution image 307 exhibits a restitution deviation 214
compared with the restituted reference 211.
Definitions and detailed description
Other characteristics and advantages of the invention will
become apparent on reading:
- of the def initions explained hereinafter of the employed
technical terms, referring to the indicative and non -limitative
examples of Figs. 1 to 17,
- of the description of Figs. 1 to 17.
Scene
Scene 3 is defined as a place in three -dimensional space,
containing objects 107 illuminated by light sources.
Image-capture appliance, image, image capture
Referring to Figs. 3 and 7, a description will now be given
of what is understood by image -capture appliance 1 and image 103.
Image-capture appliance 1 is defined as an appliance composed of
an optical system 100, of one or more sensors 101, of an
electronic unit 102 and of a memory zone 16. By means of the said
image-capture appliance 1, it is possible to obtain, from a scene
3, fixed or animated digital images 103 r ecorded in memory zone
16 or transmitted to an external device. Animated images are
composed of a succession of fixed images 103 in time. The said
image-capture appliance 1 can have the form in particular of a
photographic appliance, of a video camera, of a camera connected
to or integrated in a PC, of a camera connected to or integrated
in a personal digital assistant, of a camera connected to or
integrated in a telephone, of a videoconferencing appliance or of
a measuring camera or appliance sensitive to wavelengths other
than those of visible light, such as a thermal camera.
Image capture is defined as the method by which image 103
is calculated by image-capture appliance 1.
In the case in which an appliance is equipped with a
plurality of interchangeab le subassemblies, especially an optical
system 100, image-capture appliance 1 is defined as a special
configuration of the appliance.
Image-restitution means, restituted image,
image restitution
Referring to Fig. 2, a description will now be given of
what is understood by image -restitution means 19. Such an image -
restitution means 19 can have the form in particular of a visual
display screen, of a television screen, of a flat screen, of a
projector, of virtual reality goggles, of a printer.
Such an image-restitution means 19 is composed of:
- an electronic unit,
- one or more sources of light, of electrons or of ink,
- one or more modulators: devices for modulation of light,
of electrons or of ink,
- a focusing device, having in particular the form of an
optical system in the case of a light projector or the form of
electron-beam focusing coils in the case of a CRT screen, or the
form of filters in the case of a flat screen,
- a restitution medium 190 having in particular the form of
a screen in the case of a CRT screen, of a flat screen or of a
projector, the form of a print medium on which printing is
performed in the case of a printer, or the form of a virtual
surface in space in the case of a virtual -image projector.
By means of the said image -restitution means 19, it is
possible to obtain, from an image 103, a restituted image 191 on
restitution medium 190.
Animated images are composed of a succession of fixed
images in time.
Image restitution is defined as the method by which the
image is displayed or printed by means of image restitution means
19.
In the case in which a restitution means 19 is equipped
with a plurality of interchangeable subassemblies or of
subassemblies that can be shifted relative to one another,
especially restitution medium 190, image-restitution means 19 is
defined as a special configuration.
Sensor surface, optical center, focal distance
Referring to Fig. 1, a description will now be given of
what is defined as sensor surface 110.
Sensor surface 110 is defined as th e shape in space drawn
by the sensitive surface of sensor 101 of image -capture appliance
1 at the moment of image capture. This surface is generally
plane.
An optical center 111 is defined as a point in space
associated with image 103 at the moment of image capture. A focal
distance is defined as the distance between this point 111 and
plane 110, in the case in which sensor surface 110 is plane.
Pixel, pixel value, exposure time
Referring to Fig. 3, a description will now be given of
what is understood by pixel 104 and pixel value.
A pixel 104 is defined as an elemental zone of sensor
surface 110 obtained by creating a grid, generally regular, of
the said sensor surface 110. Pixel value is defined as a number
associated with this pixel 104.
Image capture is defined as determining the value of each
pixel 104. The set of these values constitutes image 103.
During image capture, the pixel value is obtained by
integration, over the surface of pixel 104, during a time period
defined as exposure time, of part of the light flux derived from
scene 3 via optical system 100, and by converting the result of
this integration to a digital value. The integration of the light
flux and/or the conversion of the result of this integration to a
digital value are performed by means of electronic unit 102.
This definition of the concept of pixel value is applicable
to the case of black-and-white or color images 103, whether they
be fixed or animated.
Depending on the cases, however, the part in question of
the light flux is obtained in various ways:
a) In the case of a color image 103, sensor surface 110 is
generally composed of a plurality of types of pixels 104,
associated respectively with light fluxes of different
wavelengths, examples being red, green and blue pixels .
b) In the case of a color image 103, there may also be a
plurality of sensors 101 disposed side -by-side, each receiving
part of the light flux.
c) In the case of a color image 10 3, the colors used may be
different from red, green and blue, such as for North American
NTSC television, and they may exceed three in number.
d) Finally, in the case of an interlaced television
scanning camera, the animated images produced are composed of an
alternation of images 103 containing even -numbered lines and of
images 103 containing odd-numbered lines.
Configuration used, adjustments used,
characteristics used
The configuration used is defined as the list of removable
subassemblies of image -capture appliance 1, such as optical
system 100 which, if it is interchang eable, is mounted on image -
capture appliance 1. The configuration used is characterized in
particular by:
- the type of optical system 100,
- the serial number of optical system 100 or any other
designation.
Adjustments used are defined as:
- the configuration used as defined hereinabove, as well as
the value of the manual or automatic adjustments
available in the configuration used and having an impact on the
content of image 103. These adjustments may be made by the user,
especially by means of pus hbuttons, or may be calculated by
image-capture appliance 1. These adjustments may be stored in the
appliance, especially on a removable medium, or on any device
connected to the appliance. These adjustments may include in
particular the adjustments of foc using, diaphragm and focal
length of optical system 100, the adjustments of exposure time,
the adjustments of white balance, and the integrated image -
processing adjustments, such as digital zoom, compression and
contrast.
Characteristics 74 used or set of characteristics 74 used
are defined as:
a) Parameters related to the intrinsic technical
characteristics of image -capture appliance 1, determined during
the phase of design of image -capture appliance 1. For example,
these parameters may include the formu la of optical system 100 of
the configuration used, which impacts the geometric defects and
the sharpness of the captured images; the formula of optical
system 100 of the configuration used includes in particular the
shape, the arrangement and the material of the lenses of optical
system 100.
These parameters may additionally include:
- the geometry of sensor 101, or in other words sensor
surface 110 as well as the shape and relative arrangement of
pixels 104 on this surface,
- the noise generated by electronic unit 102,
- the equation for conversion of light flux to pixel value.
b) Parameters associated with the intrinsic technical
characteristics of image -capture appliance 1, determined during
the phase of manufacture of image -capture appliance 1 and, in
particular:
- the exact positioning of the lenses in optical system 100
of the configuration used,
- the exact positioning of optical system 100 relative to
sensor 101.
c) Parameters associated with the technical characteristics
of image-capture appliance 1, determined at the moment of capture
of image 103 and, in particular:
the position and orientation of sensor surface 110
relative to scene 3,
- the adjustments used,
- the external factors, such as temperature, if they have
an influence.
d) The user's preferences, especially the color temperature
to be used for image restitution. For example, these preferences
are selected by the user by means of pushbuttons.
Observation point, observation direction
Referring to Fig. 1, a description will now be given of
what is understood by observation point 105 and observation
direction 106.
Mathematical surface 10 is defined as a surface that is
geometrically associated with sensor surface 110. For example, if
the sensor surface is plane, it will be p ossible for mathematical
surface 10 to coincide with the sensor surface.
Observation direction 106 is defined as a line passing
through at least one point of scene 3 and through optical center
111. Observation point 105 is defined as the intersection of
observation direction 106 and surface 10.
Observed color, observed intensity
Referring to Fig. 1, a description will now be given of
what is understood by observed color and observed intensity.
Observed color is defined as the color of the light emitted,
transmitted or reflected by the said scene 3 in the said
observation direction 106 at a given instant, and observed from
the said observation point 105. Observed intensity is defined as
the intensity of the light emitted by the said scene 3 in the
said observation direction 106 at the same instant, and observed
from the said observation point 105.
The color can be characterized in particular by a light
intensity that is a function of wavelength, or else by two values
as measured by a colorimeter. The int ensity can be characterized
by a value such as measured with a photometer.
The said observed color and the said observed intensity
depend in particular on the relative position of objects 107 in
scene 3 and on the illumination sources present as well as o n the
transparency and reflection characteristics of objects 107 at the
moment of observation.
Mathematical projection, mathematical image, mathematical point, mathematical
color of a point, mathematical intensity of a point, mathematical shape of a point ,
mathematical position of a point
Referring in particular to Figs. 1, 5, 9a, 9b, 9c and 9d, a
description will be given of the concepts of mathematical
projection 8, mathematical image 70, mathematical point,
mathematical color of a point, mathematical intensity of a point,
mathematical shape 41 of a point, and mathematical position 40 of
a point.
Referring to Fig. 5, a description will now be given of how
a mathematical image 70 is constructed by specified mathematical
projection 8 of at least one seen e 3 on mathematical surface 10.
Firstly, a description will be given of what is understood
by specified mathematical projection 8.
A specified mathematical projection 8 associates a
mathematical image 7 0 with:
- a scene 3 at the moment of capture of an image 103,
- and with the characteristics 74 used.
A specified mathematical projection 8 is a transformation
with which the characteristics of each point of mathematical
image 70 can be determined from scene 3 at the moment of image
capture and from the characteristics 74 used.
Mathematical projection 8 is preferentially defined in the
manner to be described hereinafter.
Mathematical position 40 of the point is defined as the
position of observation point 105 on mathematical surface 10.
Mathematical shape 41 of the point is defined as the
geometric, punctiform shape of observation point 105.
Mathematical color of the point is defined as the observed
color.
Mathematical intensity of the point is defined as the
observed intensity.
Mathematical point i s defined as the association of
mathematical position 40, mathematical shape 41, mathematical
color and mathematical intensity for the observation point 105
under consideration. Mathematical image 70 is composed of the set
of said mathematical points.
The mathematical projection 8 of scene 3 is mathematical
image 70.
Real projection, real point, real color of a point,
real intensity of a point, real shape of a point, real
position of a point
Referring in particular to Figs. 3, 5, 9a, 9b, 9c and 9d, a
description will be given hereinafter of the concepts of real
projection 72, real point, real color of a point, real intensity
of a point, real shape 51 of a point, and real position 50 of a
point.
During image capture, image -capture appliance 1 associates
an image 103 of scene 3 with the characteristics 74 used. The
light originating from scene 3 in an observation direction 106
passes through optical system 100 and arrives at sensor surface
110.
For the said observation direction, there is then obtained
what is defined as a real point, which exhibits differences
compared with the mathematical point.
Referring to Figs. 9a to 9d, a description will now be
given of the differences between the real point and the
mathematical point.
The real shape 51 associat ed with the said observation
direction 106 is not a point on the sensor surface, but it has
the form of a cloud in three -dimensional space, where it has an
intersection with one or more pixels 104. These differences are
due in particular to coma, spherical aberration, astigmatism,
grouping into pixels 104, chromatic aberration, depth of field,
diffraction, parasitic reflections and field curvature of image -
capture appliance 1. They give an impression of blurring, or of
lack of sharpness of image 103.
In addition, real position 50 associated with the said
observation direction 106 exhibits a difference compared with
mathematical position 40 of a point. This difference is due in
particular to the geometric distortion, which gives an impression
of deformation: for example, vertical walls appear to be curved.
It is also due to the fact that the number of pixels 104 is
limited, and that consequently the real position 50 can have only
a finite number of values.
In addition, the real intensity associated with the said
observation direction 106 exhibits differences compared with the
mathematical intensity of a point. These differences are due in
particular to gamma and vignetting: for example, the edges of
image 103 appear to be darker. Furthermore, noise may be add ed to
the signal.
Finally, the real color associated with the said
observation direction 106 exhibits differences compared with the
mathematical color of a point. These differences are due in
particular to gamma and the color cast. Furthermore, noise may be
added to the signal.
A real point is defined as the association of the real
position 50, the real shape 51, the real color and the real
intensity for the observation direction 106 under consideration.
The real projection 72 of scene 3 is composed of the set of
real points.
Parameterizable transformation model, parameters,
corrected image
A parameterizable transformation model 12 (or
parameterizable transformation 12 for short) is defined as a
mathematical transformation in which a corrected image 7 1 can be
obtained from an image 103 and from the value of parameters. As
indicated hereinbelow, the said parameters can in particular be
calculated from the characteristics 74 used.
By means of the said parameterizable transformation 12, it
is possible in particular to determine, for each real point of
image 103, the corrected position of the said real point, the
corrected color of the said real point, the corrected intensity
of the said real point, and the corrected shape of the said real
point, from the value of the parameters, from the real position
of the said real point and from the values of the pixels of image
103. As an example, the corrected position can be calculated by
means of polynomials of fixed degree as a function of the real
position, the coefficients of the polynomials depending on the
value of the parameters. The corrected color and the corrected
intensity can be, for example, weighted sums of the values of the
pixels, the coefficients depending on the value of the parameters
and on the real position, or else can be nonlinear functions of
the values of the pixels of image 103.
A parameterizable reverse transformation model 212 (or
parameterizable reverse transformation 212 for short) is defined
as a mathematical transformation in which an image 103 can be
obtained from a corrected image 71 and from the value of
parameters. The said parameters can be calculated in particular
from the characteristics 74 used as indicated herein below.
By means of the said parameterizable reverse transformatio n
212, it is possible in particular to determine, for each point of
the corrected image 71, the real point of image 103 corresponding
to the said point of corrected image 71, and in particular the
position of the said real point, the color of the said real
point, the intensity of the said real point, and the shape of the
said real point, from the value of the parameters and from
corrected image 71. As an example, the position of the real point
can be calculated by means of polynomials of fixed degree as a
function of the position of the point of the corrected image 71,
the coefficients of the polynomials depending on the value of the
parameters.
The parameters can include in particular: the focal length
of optical system 100 of the configuration used, or a related
value such as the position of a group of lenses, the focusing of
optical system 100 of the configuration used, or a related value
such as the position of a group of lenses, the aperture of
optical system 100 of the configuration used, or a related value
such as the position of the diaphragm.
Difference between the mathematical image and the corrected image
Referring to Fig. 5, the difference 73 between mathematical
image 70 and corrected image 71 for a given scene 3 and given
characteristics 74 used is defined as one or more values
determined from numbers characterizing the position, color,
intensity, and shape of all or part of the corrected points and
of all or part of the mathematical points.
For example, the difference 73 between mathematica 1 image
70 and corrected image 71 for a given scene 3 and given
characteristics 74 used can be determined as follows:
There are chosen characteristic points which, for
example, may be the points of an orthogonal array 80 of regularly
disposed points, as illustrated in Fig. 10.
- The difference 73 is calculated, for example, by taking,
for each characteristic point, the sum of the absolute values of
the differences between each number characterizing the corrected
position, the corrected color, the corre cted intensity and the
corrected shape respectively for the real point and for the
mathematical point. The sum function of the absolute values of
the differences may be replaced by another function such as the
mean, the sum of the squares or any other func tion with which the
numbers can be combined.
Reference scene
A reference scene 9 is defined as a scene 3 for which
certain characteristics are known. As an example, Fig. 4a shows a
reference scene 9 composed of a paper sheet bearing regularly
disposed, solid black circles. Fig. 4b shows another paper sheet
bearing the same circles, with the addition of colored lines and
areas. The circles are used to measure the real position 50 of a
point, the lines to measure the real shape 51 of a point, and the
colored areas to measure the real color of a point and the real
intensity of a point. This reference scene 9 may be composed of a
material other than paper.
Reference image
Referring to Fig. 12, a definition will now be given of the
concept of reference image 11. A reference image 11 is defined as
an image of reference scene 9 obtained with image -capture
appliance 1.
Synthetic image, synthetic -image class
Referring to Fig. 12, a definition will now be given of the
concept of synthetic image 207 and of synt hetic-image class 7. A
synthetic image 207 is defined as a mathematical image 70
obtained by mathematical projection 8 of a reference scene 9. A
synthetic-image class 7 is defined as a set of mathematical
images 70 obtained by mathematical projection 8 of one or more
reference scenes 9 for one or more sets of characteristics 74
used. In the case in which there is only one reference scene 9
and only one set of characteristics 74 used, the synthetic -image
class 7 comprises only one synthetic image 207.
Transformed image
Referring to Fig. 12, a definition will now be given of the
concept of transformed image 13. A transformed image 13 is
defined as the corrected image obtained by application of a
parameterizable transformation model 12 to a reference image 1 1.
Transformed image close to a synthetic -image class,
deviation
Referring to Fig. 12, a description will now be given of
the concept of transformed image 13 close to a synthetic -image
class 7 and of the concept of deviation 14.
The difference between a transformed image 13 and a
synthetic-image class 7 is defined as the smallest difference
between the said transformed image 13 and any one of the
synthetic images 207 of the said synthetic -image class 7.
Referring to Fig. 12, a description will next be given of a
fourth algorithm with which it is possible to choose, among the
parameterizable transformation models 12, that with which each
reference image 11 can be transformed to a transformed image 13
close to the synthetic -image class 7 of the reference scene 9
corresponding to the said reference image 11, in different cases
of reference scenes 9 and characteristics 74 used.
- In the case of a given reference scene 9 associated with
a set of given characteristics 74 used, there is chosen the
parameterizable transformation 12 (and its parameters) with which
the reference image 11 can be transformed to the transformed
image 13 that exhibits the smallest difference compared with
synthetic-image class 7. Synthetic-image class 7 and transformed
image 13 are then said to be close. Deviation 14 is defined as
the said difference.
- In the case of a group of given reference scenes
associated with sets of given characteristics 74 used, the
parameterizable transformation 12 (and its parameters) is chosen
as a function of the differences between the transformed image 13
of each reference scene 9 and the synthetic -image class 7 of each
reference scene 9 under consideration. There is chosen the
parameterizable transformation 12 (and its parameters) with which
the reference images 11 can be transformed to transformed images
13 such that the sum of the said differences is minimized. The
sum function may be replaced by another function such as the
product. Synthetic-image class 7 and transformed images 13 are
then said to be close. Deviation 14 is defined as a value
obtained from the said differences, for example by calculating
the mean thereof.
- In the case in which certain characteristics 74 used are
unknown, it is possible to determine them from the capture of a
plurality of reference images 11 of at least one reference scene
9. In this case, there are simultaneously determined the unknown
characteristics and the parameterizable transformation 12 (and
its parameters) with which the reference images 11 can be
transformed to transformed images 13, such that the sum of the
said differences is minimized, in particular by iterative
calculation or by solving equations concerning the sum of the
said differences and/or their product and/or any other
appropriate combination of the said differences. Synthetic -image
class 7 and transformed images 13 are then said to be close. The
unknown characteristics may be, for example, the relative
positions and orientations of sensor surface 110 and of each
reference scene 9 under consideration . Deviation 14 is defined as
a value obtained from the said differences, for example by
calculating the mean thereof. Referring to Fig. 12, a description
will next be given of a first calculation algorithm with which a
choice can be made:
- within a set of parameterizable transformation models,
- within a set of parameterizable reverse transformation
models,
- within a set of synthetic images,
- within a set of reference scenes and within a set of
transformed images.
This choice is based on:
- a reference scene 9, and/or
- a transformed image 13, and/or
- a parameterizable transformation model 12 with which the
reference image 11 obtained by capturing reference scene 9 by
means of image-capture appliance 1 can be transformed to
transformed image 13, and/or
- a parameterizable reverse transformation model 212 with
which transformed image 13 can be transformed to reference image
11, and/or
- a synthetic image 207 obtained from reference scene 9
and/or obtained from reference image 11.
The choice adopted is that which minimizes the difference
between transformed image 13 and synthetic image 207. Synthetic
image 207 and transformed image 13 are then said to be close.
Deviation 14 is defined as the said difference.
Preferably, according to the inventio n, it is possible by
means of the first calculation algorithm to choose, within a set
of mathematical projections, one mathematical projection 8 with
which synthetic image 207 can be constructed from reference scene
9.
Referring to Fig. 12, a description will next be given of a
second calculation algorithm that includes the stages of:
- choosing at least one reference scene 9,
capturing at least one reference image 11 of each
reference scene 9 by means of image -capture appliance 1.
This second algorithm additionally includes the stage of
choosing, within a set of parameterizable transformation models
and within a set of synthetic images:
a parameterizable transformation model 12 with which
reference image 11 can be transformed to a transformed image 13,
and/or
- a synthetic image 207 obtained from reference scene 9
and/or obtained from reference image 11,
The choice adopted is that which minimizes the difference
between transformed image 13 and synthetic image 207. Synthetic
image 207 and transformed image 13 are then said to be close.
Deviation 14 is defined as the said difference.
Preferably, according to the invention, it is possible by
means of the second calculation algorithm to choose, within a set
of mathematical projections, one mathematica 1 projection 8 with
which synthetic image 207 can be constructed from reference scene
9.
Best transformation
The best transformation is defined as:
- the transformation with which, among the parameterizable
transformation models 12, each reference imag e 11 can be
transformed to a transformed image 13 close to synthetic -image
class 7 of the reference scene 9 corresponding to the said
reference image 11, and/or,
- the parameterizable transformation models 12 among which
the parameterizable transformation models, such as the
transformed image 13, are close to synthetic image 207, and/or
the parameterizable reverse transformation models 212
among which the parameterizable reverse models, such as the
transformed image 13, are close to the synthetic image 207.
Calibration
Calibration is defined as a method with which data related
to the intrinsic characteristics of image -capture appliance 1 can
be obtained, for one or more configurations used, each composed
of an optical system 100 associated with an ima ge-capture
appliance 1.
Case 1: in the case in which there is only one
configuration, the said method includes the following stages:
- the stage of mounting the said optical system 100 on the
said image-capture appliance 1,
- the stage of choosing one o r more reference scenes 9,
- the stage of choosing several characteristics 74 used,
- the stage of capturing images of the said reference
scenes 9 for the said characteristics used,
- the stage of calculating the best transformation for each
group of re f erence scenes 9 corresponding to the same
characteristics 74 used.
Case 2: in the case in which all the configurations
corresponding to a given image -capture appliance 1 and to all
optical systems 100 of the same type are taken into
consideration, the said method includes the following stages:
- the stage of choosing one or more reference scenes 9,
- the stage of choosing several characteristics 74 used,
- the stage of calculating images 103 from characteristics
74 used and in particular from formulas for optical system 100 of
the configuration used and from values of parameters, by means,
for example, of software for calculating the optical system by
ray tracing,
- the stage of calculating the best transformation for each
group of reference scenes 9 corresponding to the same
characteristics used.
Case 3: in the case in which all the configurations
corresponding to a given optical system 100 and to all the image -
capture appliances 1 of the same type are taken into
consideration, the said method includ es the following stages:
- the stage of mounting the said optical system 100 on an
image-capture appliance 1 of the type under consideration,
- the stage of choosing one or more reference scenes 9,
- the stage of choosing several characteristics 74 used ,
- the stage of capturing images of the said reference
scenes 9 for the said characteristics used,
- the stage of calculating the best transformation for each
group of reference scenes 9 corresponding to the same
characteristics used.
Calibration can be performed preferentially by the
manufacturer of image -capture appliance 1, for each appliance and
configuration in case 1. This method is more precise but imposes
more limitations and is highly suitable in the case in which
optical system 100 is not inte rchangeable.
Alternatively, calibration can be performed by the
manufacturer of image-capture appliance 1, for each appliance
type and configuration in case 2. This method is less precise but
is simpler.
Alternatively, calibration can be performed by the
manufacturer of image -capture appliance 1 or by a third party,
for each optical system 100 and type of appliance in case 3. This
method is a compromise in which one optical system 100 can be
used on all image-capture appliances 1 of one type, without
repeating the calibration for each combination of image -capture
appliance 1 and optical system 100. In the case in which an
image-capture appliance has a non -interchangeable optical system,
the method permits the calibration to be performed only one time
for a given type of appliance.
Alternatively, calibration can be performed by the
appliance seller or installer, for each image -capture appliance 1
and configuration in case 1.
Alternatively, calibration can be performed by the
appliance seller or installer, for each optical system 100 and
type of appliance in case 3.
Alternatively, calibration can be performed by the
appliance user, for each appliance and configuration in case 1.
Alternatively, calibration can be performed by the
appliance user, for each op tical system 100 and type of appliance
in case 3.
Design of the digital optical system
Design of the digital optical system is defined as a method
for reducing the cost of optical system 100, by:
designing an optical system 100 having defects,
especially in positioning of real points, or choosing the same
from a catalog,
- reducing the number of lenses, and/or
- simplifying the shape of the lenses, and/or
- using less expensive materials, processing operations or
manufacturing processes.
The said method includes the following stages:
- the stage of choosing an acceptable difference (within
the meaning defined hereinabove),
- the stage of choosing one or more reference scenes 9,
- the stage of choosing several characteristics 74 used.
The said method also includes iteration of the following
stages:
- the stage of choosing an optical formula that includes in
particular the shape, material and arrangement of the lenses,
the stage of calculating images 103 from the
characteristics 74 used and i n particular from the formulas for
optical system 100 of the configuration used, by employing, for
example, software for calculating the optical system by ray
tracing, or by making measurements on a prototype,
- the stage of calculating the best transform ation for each
group of reference scenes 9 corresponding to the same
characteristics 74 used,
- the stage of verifying if the difference is acceptable,
until the difference is acceptable.
Formatted information
Formatted information 15 associated with i mage 103, or
formatted information 15, is defined as all or part of the
following data:
- data related to the intrinsic technical characteristics
of image-capture appliance 1, especially the distortion
characteristics, and/or
- data related to the techni cal characteristics of image -
capture appliance 1 at the moment of image capture, especially
the exposure time, and/or
data related to the preferences of the said user,
especially the color temperature, and/or
- data related to the deviations 14.
Database of characteristics
A database 22 of characteristics is defined as a database
containing formatted information 15 for one or more image -capture
appliances 1 and for one or more images 103.
The said database 22 of characteristics can be stored in
centralized or distributed manner, and in particular can be:
- integrated into image-capture appliance 1,
- integrated into optical system 100,
- integrated into a removable storage device,
- integrated into a PC or other computer connected to the
other elements during image capture,
- integrated into a PC or other computer connected to the
other elements after image capture,
- integrated into a PC or other computer capable of reading
a storage medium shared with image -capture appliance 1,
- integrated into a remote server connected to a PC or
other computer, itself connected to the other image -capture
elements.
Fields
Referring to Fig. 8, a definition will now be given of the
concept of fields 91. The formatted information 15 associated
with image 10 3 can be recorded in several forms and structured
into one or more tables, but it corresponds logically to all or
part of fields 91, comprising:
(a) the focal distance,
(b) the depth of field
(c) the geometric defects.
The said geometric defects include geometric defects of
image 103 characterized by the parameters associated with the
filming characteristics 74 and a parameterizable transformation
representing the characteristics of image -capture appliance 1 at
the moment of filming. By means of the sa id parameters and of the
said parameterizable transformation, it is possible to calculate
the corrected position of a point of image 103.
The said geometric defects also include the vignetting
characterized by the parameters associated with filming
characteristics 74 and a parameterizable transformation
representing the characteristics of image -capture appliance 1 at
the moment of filming. By means of the said parameters and the
said parameterizable transformation, it is possible to calculate
the corrected intensity of a point of image 103.
The said geometric defects also include the color cast
characterized by the parameters associated with filming
characteristics 74 and a parameterizable transformation
representing the characteristics of image -capture appliance 1 at
the moment of filming. By means of the said parameters and the
said parameterizable transformation, it is possible to calculate
the corrected color of a point of image 103.
The said fields 91 also include (d) the sharpness of image
103.
The said sharpness includes the blurring in resolution of
image 103 characterized by the parameters associated with filming
characteristics 74 and a parameterizable transformation
representing the characteristics of image -capture appliance 1 at
the moment of filming. By means of the said parameters and the
said parameterizable transformation, it is possible to calculate
the corrected shape of a point of image 103. Blurring covers in
particular coma, spherical aberration, astigmatism, grouping into
pixels 104, chromatic aberration, depth of field, diffraction,
parasitic reflections and field curvature.
The said sharpness also includes the blurring in depth of
field, in particular spherical aberrations, coma and astigmatism.
The said blurring depends on the dista nee of the points of scene
3 relative to image-capture appliance 1, and it is characterized
by the parameters associated with filming characteristics 74 and
a parameterizable transformation representing the characteristics
of image-capture appliance 1 at the moment of filming. By means
of the said parameters and of the said parameterizable
transformation, it is possible to calculate the corrected shape
of a point of image 103.
The said fields 91 also include (e) parameters of the
quantization method. The s aid parameters depend on the geometry
and physics of sensor 101, on the architecture of electronic unit
102 and on any processing software that may be used.
The said parameters include a function that represents the
variations of intensity of a pixel 104 as a function of
wavelength and light flux derived from the said scene 3. The said
function includes in particular gamma information.
The said parameters also include:
- the geometry of the said sensor 101, especially the
shape, the relative position and the number of sensitive elements
of the said sensor 101,
- a function representative of the spatial and temporal
distribution of noise of image -capture appliance 1,
- a value representative of the exposure time for image
capture.
The said fields 91 also include (f) parameters of the
digital-processing operations performed by image -capture
appliance 1, especially digital zoom and compression. These
parameters depend on the processing software of image -capture
appliance 1 and on the user's adjustments.
The said fields 91 also include:
(g) parameters representative of the user's preferences,
especially as regards the degree of blurring and the resolution
of image 103.
(h) the deviations 14.
Calculation of formatted information
The formatted information 15 can be calculated and recorded
in database 22 in several stages.
a) A stage at the end of design of image -capture appliance
1.
By means of this stage it is possible to obtain intrinsic
technical characteristics of image -capture appliance 1, and in
particular:
the spatial and temporal distribution of the noise
generated by electronic unit 102,
- the formula for conversion of light flux to pixel value,
- the geometry of sensor 101.
b) A stage at the end of calibration or design of the
digital optical system.
By means of this stage it is possible to obtain other
intrinsic technical characteristics of image -capture appliance 1,
and in particular, for a certain number of values of
characteristics used, the best associated transformation and the
associated deviation 14.
c) A stage in which the user's preferences are chosen by
means of pushbuttons, menus or removable media, or of connection
to another device.
d) An image capture stage.
By means of this stage (d) it is possible to obtain
technical characteristics of image-capture appliance 1 at the
moment of image capture, and in particular the exposure time,
which is determined by the manual or automatic adjustments made.
By means of stage (d) it is also possible to obtain the
focal distance. The focal distance is calculated from:
- a measurement of the position of the group of lenses of
variable focal length of optical system 100 of the configuration
used, or
- a set value input to the positioning motor, or
- a manufacturer's value if the focal 1 ength is fixed.
The said focal distance can then be determined by analysis
of the content of image 103.
By means of stage (d) it is also possible to obtain the
depth of field. The depth of field is calculated from:
- a measurement of the position of the group of focusing
lenses of optical system 100 of the configuration used, or
- a set value input to the positioning motor, or
- a manufacturer's value if the depth of field is fixed.
By means of stage (d) it is also possible to obtain the
defects of geometry and of sharpness. The defects of geometry and
of sharpness correspond to a transformation calculated by means
of a combination of transformations of the database 22 of
characteristics obtained at the end of stage (b) . This
combination is chosen to r epresent the values of parameters
corresponding to the characteristics 74 used, especially the
focal distance.
By means of stage (d) it is also possible to obtain the
parameters of digital processing performed by image -capture
appliance 1. These parameters are determined by the manual or
automatic adjustments made.
The calculation of formatted information 15 according to
stages (a) to (d) can be performed by:
a device or software integrated into image -capture
appliance 1, and/or
- driver software in a PC or other computer, and/or
- software in a PC or other computer, and/or
- a combination of the three.
The foregoing transformations in stage (b) and stage (d)
can be stored in the form of:
- a general mathematical formula,
- a mathematical formula for each point,
- a mathematical formula for certain characteristic points.
The mathematical formulas can be described by:
- a list of coefficients,
- a list of coefficients and coordinates.
By means of these different methods it is possible to reach
a compromise between the size of the memory available for storage
of the formulas and the calculating power available for
calculation of the corrected images 71.
In addition, in order to retrieve the data, identifiers
associated with the data are recorde d in database 22. These
identifiers include in particular:
- an identifier of the type and of the reference of image -
capture appliance 1,
- an identifier of the type and of the reference of optical
system 100, if it is removable,
- an identifier of the type and of the reference of any
other removable element having a link to the stored information,
- an identifier of image 103,
- an identifier of the formatted information 15.
As described by Fig. 11, a completed image 120 is defined
as the image 103 associated with the formatted information 15.
This completed image 120 can preferentially have the form of a
file P100, as described by Fig. 14. Completed image 120 can also
be distributed into a plurality of files.
Completed image 120 can be calculated by image-capture
appliance 1. It can also be calculated by an external calculating
device, such as a computer.
Image-processing software
Image-processing software 4 is defined as software that
accepts one or more completed images 120 as input and that
performs processing operations on these images. These processing
operations can include in particular:
- calculating a corrected image 71,
- performing measurements in the real world,
- combining several images,
- improving the fidelity of the images relative to the real
world,
- improving the subjective quality of images,
- detecting objects or persons 107 in a scene 3,
- adding objects or persons 107 to a scene 3,
- replacing or modifying objects or persons 107 in a scene
3,
- removing shadows from a scene 3,
- adding shadows to a scene 3,
- searching for objects in an image base.
The said image-processing software can be:
- integrated into image-capture appliance 1,
- run on calculating means 17 connected to image -capture
appliance 1 by transmission means 18.
Digital optical system
A digital optical system is defined as the combination of
an image-capture appliance 1, a database 22 of characteristics
and a calculating means 17 that permits:
- image capture of an image 103,
- calculation of the completed image,
- calculation of the corrected image 71.
Preferentially, the user obtains corrected image 71
directly. If he wishes, the user may demand suppression of
automatic correction.
The database 22 of characteristics may be:
- integrated into image-capture appliance 1,
- integrated into a PC or other computer connected to the
other elements during image capture,
- integrated into a PC or other computer connected to the
other elements after image capture,
- integrated into a PC or other computer capable of reading
a storage medium shared with image -capture appliance 1,
- integrated into a remote server connected to a PC or
other computer, itself connected to the other image -capture
elements.
Calculating means 17 may be :
- integrated onto a component together with sensor 101,
integrated onto a component together with part of
electronics unit 102,
- integrated into image -capture appliance 1,
- integrated into a PC or other computer connected to the
other elements during image capture,
- integrated into a PC or other computer connected to the
other elements after image capture,
- integrated into a PC or other computer capable of reading
a storage medium shared with image -capture appliance 1,
- integrated into a re mote server connected to a PC or
other computer, itself connected to the other image -capture
elements.
Processing of the complete chain
The foregoing paragraphs have essentially presented precise
details of the concepts and description of the method and system
according to the invention for providing, to image -processing
software 4, formatted information 15 related to the
characteristics of image-capture appliance 1.
In the paragraphs to follow, an expanded definition will be
given of the concepts and a supplemented description will be
given of the method and system according to the invention for
providing, to image-processing software 4, formatted information
15 related to the characteristics of image -restitution means 19.
In this way the processing of a complete chain will be explained.
By means of the processing of the complete chain, it is
possible:
- to improve the quality of image 103 from one end of the
chain to the other, to obtain a restituted image 191 while
correcting the defects of image -capture appliance 1 and of image -
restitution means 19, and/or
- to use optical systems of lower quality and of lower cost
in a video projector in combination with software for improvement
of image quality.
Definitions associated with the image -restitution means
On the basis of Figs. 2, 17 and 6, a description will now
be given of how the characteristics of an image -restitution means
19 such as a printer, a visual display screen or a projector are
taken into account in the formatted information 15.
The supplements or modifications to be made to the
definitions in the case of an image -restitution means 19 may be
inferred by analogy by a person skilled in the art by analogy
with the definitions provided in the case of an image -capture
appliance 1. Nevertheless, in order to illustrate this method, a
description with reference in particular to Fig. 6 and Fig. 17
will now be given of the main supplements or modifications.
By restitution characteristics 95 used there are designated
the intrinsic characteristics of image-restitution means 19, the
characteristics of image -restitution means 19 at the moment of
image restitution, and the user's preferences at the moment of
image restitution. In the case of a projector in particular, the
restitution characteristics 95 used include the shape and
position of the screen used.
By parameterizable restitution transformation model 97 (or
parameterizable restitution transformation 97 for short), there
is designated a mathematical transformation similar to
parameterizable transformation model 12. By parameterizable
reverse restitution transformation model 297 (or parameterizable
reverse restitution transformation 297 for short), there is
designated a mathematical transformation similar to
parameterizable reverse transformation model 212.
By corrected restitution image 94 there is designated the
image obtained by application of parameterizable restitution
transformation 97 to image 103.
By mathematical restitution projection 96 there is
designated a mathematical projection that associates, with a
corrected restitution image 94, a mathematical restitution image
92 on the mathematical restitution surface geometrically
associated with the surface of restitution medium 190. The
mathematical restitution points of the mathematical res titution
surface have a shape, position, color and intensity calculated
from corrected restitution image 94.
By real restitution projection 90 there is designated a
projection that associates a restituted image 191 with an image
103. The pixel values of image 103 are converted by the
electronic unit of restitution means 19 to a signal that drives
the modulator of restitution means 19. Real restitution points
are obtained on restitution medium 190. The said real restitution
points are characterized by shape , color, intensity and position.
The phenomenon of grouping into pixels 104 described hereinabove
in the case of an image -capture appliance 1 does not occur in the
case of an image-restitution means. On the other hand, an inverse
phenomenon occurs, with the result in particular that lines take
on a staircase appearance.
Restitution difference 93 is designated as the difference
between restituted image 191 and mathematical restitut ion image
92. This restitution difference 93 is obtained by analogy with
difference 73.
By restitution reference 209 there is designated an image
103 in which the values of pixels 104 are known. By restituted
reference 211 there is designated the restituted image 191
obtained by mathematical restitution projection 90 of restitutio n
reference 209. By corrected reference restitution image 213,
there is designated the corrected restitution image 94
corresponding to restitution reference 209 for parameterizable
restitution transformation model 97 and/or for parameterizable
reverse restitution transformation model 297. By synthetic
restitution image 307 there is designated the mathematical
restitution image 92 obtained by mathematical restitution
projection 96 of corrected reference restitution image 213.
By best restitution transformat ion there is designated:
for a restitution reference 209 and the restitution
characteristics 95 used, that with which image 103 can be
transformed to a corrected restitution image 94 such that its
mathematical restitution projection 92 exhibits the minimum
restitution difference 93 compared with restituted image 191,
and/or
- the parameterizable restitution transformation 97 among
the parameterizable restitution transformation models such that
restituted reference 211 exhibits the minimum restitution
difference 93 compared with the synthetic restitution image 307,
and/or
- the parameterizable reverse restitution transformation
297 among the parameterizable reverse transformation models such
that the restituted reference 211 exhibits the minimum
restitution difference 93 compared with the synthetic restitution
image 307.
The restituted reference 211 and the synthetic restitution
image 307 are then said to be close.
The methods of restitution calibration and of design of the
digital optical restitution system are comparable with the
methods of calibration and of design of the digital optical
system in the case of an image -capture appliance 1. Nevertheless,
differences are present in certain stages, and in particular the
following stages:
- the stage of choosing a restitution reference 209;
the stage of performing restitution of the said
restitution reference;
- the stage of calculating the best restitution
transformation.
Preferably, according to the invention, the method includes
a sixth algorithm for calculation of the formatted information
15. By means of this sixth algorithm it is possible to make a
choice:
within a set of parameterizable restitution
transformation models,
within a set of parameterizable reverse restitution
transformation models,
- within a set of mathematical restitution projections,
- within a set of restitution references and within a set
of corrected reference restitution images.
The choice made by this sixth algorithm is based on:
- a restitution reference 209, and /or
- a corrected reference restitution image 213, and/or
- a parameterizable restitution transformation model 97
with which the restitution reference 209 can be transformed to
the corrected reference restitution image 213, and/or
a parameterizable re verse restitution transformation
model 297 with which the corrected reference restitution image
213 can be transformed to the restitution reference 209, and/or
- a mathematical restitution projection 96 with which a
synthetic restitution image 307 can be constructed from the
corrected reference restitution image 213.
The choice is made by this sixth algorithm in such a way
that the synthetic restitution image 307 is close to the
restituted reference 211 obtained by restitution of restitution
reference 209 by means of image-restitution means 19. Restituted
reference 211 exhibits a restitution deviation 214 compared with
synthetic restitution image 307.
According to an alternative embodiment of the invention,
the method includes a seventh algorithm for calculation of the
formatted information. This seventh algorithm includes the stages
of:
- choosing at least one restitution reference 209,
- restituting restitution reference 209 to a restituted
reference 211 by means of image -restitution means 19.
By means of this seventh algorithm it is also possible to
choose, within a set of parameterizable restitution
transformation models and within a set of mathematical
restitution projections:
- a parameterizable restitution transformation model 97
with which restitution reference 209 can be transformed to a
corrected reference restitution image 213, and
- a mathematical restitution projection 96 with which a
synthetic restitution image 307 can be constructed from corrected
reference restitution image 213.
The choice is made by the seventh algorithm in such a way
that synthetic restitution image 307 is close to restituted
reference 211. The restituted reference exhibits a restitution
deviation 214 compared with the synthetic restitution image 307.
By means of parameterizable reverse restitution transformation
model 297, it is possible to transform corrected reference
restitution image 213 to restitution reference 209.
According to another alternative embodiment of the
invention, the method includes an eighth algor ithm for
calculation of the formatted information. This eighth algorithm
includes the stage of choosing a corrected reference restitution
image 213. This eighth algorithm also includes the stage of
making a choice within a set of parameterizable restitutio n
transformation models, within a set of mathematical restitution
projections and within a set of restitution references. This
choice is based on:
- a restitution reference 209, and/or
- a parameterizable restitution transformation model 97
with which restitution reference 209 can be transformed to
corrected reference restitution image 213, and/or
a parameterizable reverse restitution transformation
model 297 with which the corrected reference restitution image
213 can be transformed to the restitution reference 209, and/or
- a mathematical restitution projection 96 with which a
synthetic restitution image 307 can be constructed from the
corrected reference restitution image 213.
The eighth algorithm makes this choice in such a way that
synthetic restitution image 307 is close to restituted reference
211 obtained by restitution of restitution reference 209, by
means of image-restitution means 19. Restituted reference 211
exhibits a restitution deviation compared with synthetic
restitution image 307.
Preferably, according to the invention, the method includes
a ninth algorithm for calculating the restitution deviations 214.
This ninth algorithm includes the stages of:
calculating the restitution deviations 214 between
restituted reference 211 and syn thetic restitution image 307,
- associating restitution deviations 214 with formatted
information 15.
It results from the combination of technical features that
it is possible to verify automatically, for example during
manufacture of the appliance, that the method has produced
formatted information within acceptable tolerances.
The formatted information 15 related to an image -capture
appliance 1 and that related to an image -restitution means 19 can
be used end-to-end for the same image.
It is also possible to combine the formatted information 15
related to each of the appliances to obtain formatted information
15 related to the appliance chain, for example by addition of a
vector field, in the case of geometric distortion.
In the foregoing, a descript ion was given of the concept of
field in the case of an image -capture appliance 1. This concept
is also applicable by analogy in the case of image -restitution
means 19. Nonetheless the parameters of the quantization method
are replaced by the parameters of the signal-reconstitution
method, meaning: the geometry of restitution medium 190 and its
position, a function representing the spatial and temporal
distribution of the noise of image -restitution means 19.
In an alternative embodiment according to the in vention,
restitution means 19 is associated with an image -capture
appliance 1 to restitute, in digital form, restituted reference
211 from restitution reference 209. The method is such that, to
produce the formatted information 15 related to the defects P5 of
restitution means 19, the formatted information 15 related to
image-capture appliance 1 associated with the restitution means
is used, for example, to correct the defects of image -capture
appliance 1 in such a way that restituted reference 211 contains
only the defects P5 of restitution means 19.
Generalization of the concepts
The technical features of which the invention is composed
and which are specified in the claims have been defined,
described and illustrated by referring essentially to image -
capture appliances of digital type, or in other words appliances
that produce digital images. It can be easily understood that the
same technical features are applicable in the case of image -
capture appliances that would be the combination of an appliance
based on silver technology (a photographic or cinematographic
appliance using sensitive silver halide films, negatives or
reversal films) with a scanner for producing a digital image from
the developed sensitive films. Certainly it is appropriate in
this case to adapt at least some of the definitions used. Such
adaptations are within the capability of the person skilled in
the art. In order to demonstrate the obvious character of such
adaptations, it is merely necessary to mention that the concepts
of pixel and pixel value illustrated by referring to Fig. 3 must,
in the case of the combination of an appliance based on silver
technology with a scanner, be applied to an elemental zone of the
surface of the film after this has been digitized by means of the
scanner. Such transpositions of definitions are self -evident and
can be extended to the concept of the configuration used. As an
example, the list of removable subassemblies of image -capture
appliance 1 included in the configuration used can be
supplemented by the type of photographic film effectively used in
the appliance based on silver technology.
Other characteristics and advantages of the invention will
become clear upon reading the indicative and non -limitative
definitions and examples explained hereinaf ter with reference to
Figs. 1 to 17.
Appliance
Referring in particular to Figs. 2, 3 and 13, a description
will be given of the concept of appliance P25. Within the meaning
of the invention, an appliance P25 may be in particular:
- an image-capture appliance 1, such as a disposable photo
appliance, a digital photo appliance, a reflex appliance, a
scanner, a fax machine, an endoscope, a camcorder, a surveillance
camera, a game, a camera integrated into or connected to a
telephone, to a personal digital a ssistant or to a computer, a
thermal camera or an echographic appliance,
- an image-restitution appliance 19 or image -restitution
means 19, such as a screen, a projector, a television set,
virtual-reality goggles or a printer,
an appliance, including i ts installation, such as a
projector, a screen and the manner in which they are positioned.
- the positioning of an observer relative to an image -
restitution appliance 19, which introduc es parallax errors in
particular,
- a human being or observer havin g vision defects, such as
astigmatism,
an appliance which it is hoped can be emulated, to
produce images having, for example, an appearance similar to
those produced by an appliance of the Leica brand,
- an image-processing device, such as zoom softwar e, which
has the edge effect of adding blurring,
a virtual appliance equivalent to a plurality of
appliances P25,
A more complex appliance P25, such as a
scanner/fax/printer, a photo -printing Minilab, or a
videoconferencing appliance can be regarded as an appliance P25
or as a plurality of appliances P25.
Appliance chain
Referring in particular to Fig. 13, a description will now
be given of the concept of appliance chain P3. An appliance chain
P3 is defined as a set of appliances P25. The concept of
appliance chain P3 may also include a concept of order.
The following examples constitute appliance chains P3:
- a single appliance P25,
- an image-capture appliance 1 and an image -restitution
appliance 19,
- a photo appliance, a scanner or a printer, f or example in
a photo-printing Minilab,
- a digital photo appliance or a printer, for example in a
photo-printing Minilab,
a scanner, a screen or a printer, for example in a
computer,
- a screen or projector, and the eye of a human being,
- one appliance and another appliance which it is hoped can
be emulated,
- a photo appliance and a scanner,
- an image-capture appliance and image-processing software,
image-processing software and an image -restitution
appliance 19,
- a combination of the prec eding examples,
- another set of appliances P25.
Defect
Referring in particular to Fig. 13, a description will now
be given of the concept of defect P5. A defect P5 of appliance
P25 is defined as a defect related to the characteristics of the
optical system and/or of the sensor and/or of the electronic unit
and/or of the software integrated in an appliance P25; examples
of defects P5 include geometric defects, sharpness defects,
colorimetry defects, geometric distortion defects, geometric
chromatic aberration defects, geometric vignetting defects,
contrast defects, colorimetry defects, in particular rendering of
colors and color cast, defects of flash uniformity, sensor noise,
grain, astigmatism defects and spherical aberration defects.
Image
Referring in particular to Figs. 2, 5, 6 and 13, a
description will now be given of the concept of image 103. Image
103 is defined as a digital image captured or modified or
restituted by an appliance P25. Image 103 may originate from an
appliance P25 of appliance chain P3. Image 103 may be addressed
to an appliance P25 of appliance chain P3. More generally, image
103 may be derived from and/or addressed to appliance chain P3.
In the case of animated images, such as video images, composed of
a time sequence of fixe d images, image 103 is defined as one
fixed image of the sequence of images.
Formatted information
Referring in particular to Figs. 7, 8, 10 and 13, a
description will now be given of the concept of formatted
information 15. Formatted information 15 is defined as data
related to the defects P5 or characterizing the defects P5 of one
or more appliances P25 of appliance chain P3 and enabling image -
processing means PI to modify the quality of images 103 by making
allowance for the defects P5 of appliance P2 5.
To produce the formatted information 15, there can be used
various methods and systems based on measurements and/or
simulations and/or calibrations, such as, for example, the
calibration method described hereinabove.
To transmit the formatted informat ion 15, there can be used
a file P100 containing the completed image 120. As an example, an
image-capture appliance 1 such as a digital photo appliance can
produce files containing image 103, formatted information 15
copied from an internal memory of the a ppliance, and data in Exif
format containing the adjustments used.
To produce the formatted information 15, it is possible,
for example, to use the method and the system described in the
International Patent Application filed on the same day as the
present application in the name of Vision IQ and entitled "Method
and system for producing formatted information related to
geometric distortions". That application describes a method for
producing formatted information 15 related to the appliances P25
of an appliance chain P3. Appliance chain P3 is composed in
particular of at least one image -capture appliance 1 and/or at
least one image-restitution appliance 19. The method includes the
stage of producing formatted information 15 related to the
geometric distortions of at least one appliance P25 of the chain.
Appliance P25 preferably makes it possible to capture or
restitute an image on a medium. Appliance P25 contains at least
one fixed characteristic and/or one variable characteristic
depending on the image. The fixed characteristic and/or variable
characteristic can be associated with one or more values of
characteristics, especially the focal length and/or the focusing
and their values of associated characteristics. The method
includes the stage of producing, from a measured field, measured
formatted information related to the geometric distortions of the
appliance. The formatted information 15 may include the measured
formatted information.
To produce the formatted information 15, it is possible,
for example , to use the method and the system described in the
International Patent Application filed on the same day as the
present application in the name of Vision IQ and entitled "Method
and system for producing formatted information related to the
defects of at least one appliance of a chain, especially to
blurring". That application describes a method for producing
formatted information 15 related to the appliances P25 of an
appliance chain P3. Appliance chain P3 is composed in particular
of at least one image -capture appliance and/or at least one
image-restitution appliance 19. The method includes the stage of
producing formatted information 15 related to the defects P5 of
at least one appliance P25 of the chain. Preferably, appliance
P25 with which an image can be captured or restituted contains at
least one fixed characteristic and/or one variable characteristic
depending on the image (I) . The fixed and/or variable
characteristics can be associated with one or more values of
characteristics, especially the foca 1 length and/or the focusing
and their values of associated characteristics. The method
includes the stage of producing measured formatted information
related to the defects P5 of appliance P25 from a measured field.
The formatted information 15 may includ e the measured formatted
information.
To produce the formatted information 15, it is possible,
for example, to use the method and the system described in the
International Patent Application filed on the same day as the
present application in the name of Vision IQ and entitled "Method
and system for reducing update frequency of image processing
means". That application describes a method for reducing the
update frequency of image-processing means PI, in particular
software and/or a component. The image -processing means make it
possible to modify the quality of the digital images derived from
or addressed to an appliance chain P3. Appliance chain P3 is
composed in particular of at least one image -capture appliance
and/or at least one image-restitution appliance 19. Image-
processing means PI employ formatted information 15 related to
the defects P5 of at least one appliance of appliance chain P 5.
The formatted information 15 depends on at least one variable.
The formatted information 15 makes it possible to es tablish a
correspondence between one part of the variables and of the
identifiers. By means of the identifiers it is possible to
determine the value of the variable corresponding to the
identifier by taking the identifier and the image into account.
It results from the combination of technical features that it is
possible to determine the value of a variable, especially in the
case in which the physical significance and/or the content of the
variable are known only after distribution of image -processing
means PI. It also results from the combination of technical
features that the time between two updates of the correction
software can be spaced apart. It also results from the
combination of technical features that the various economic
players that produce ap pliances and/or image -processing means can
update their products independently of other economic players,
even if the latter radically change the characteristics of their
product or are unable to force their client to update their
products. It also results from the combination of technical
features that a new functionality can be deployed progressively
by starting with a limited number of economic players and pioneer
users.
To search for the formatted information 15, it is possible,
for example, to use the method and the system described in the
International Patent Application filed on the same day as the
present application in the name of Vision IQ and entitled "Method
and system for modifying the quality of at least one image
derived from or addressed to an appliance chain". That
application describes a method for modifying the quality of at
least one image 103 derived from or addressed to a specified
appliance chain. The specified appliance chain is composed of at
least one image-capture appliance and/or at least one image -
restitution appliance 19. The image -capture appliances and/or the
image-restitution appliances being progressively introduced on
the market by separate economic players belong to an
indeterminate set of appliances. The appliances P25 of the set of
appliances exhibit defects P5 that can be characterized by
formatted information 15. For the image in question, the method
includes the following stages:
- the stage of compiling directories of the sources of
formatted information related to th e appliances P25 of the set of
appliances,
the stage of automatically searching for specific
formatted information related to the specified appliance chain
among the formatted information 15 compiled in this way,
- the stage of automatically modifying the image by means
of image-processing software and/or image -processing components,
while taking into account the specific formatted information
obtained in this way.
To exploit the formatted information 15, it is possible,
for example, to use the method and the system described in the
International Patent Application filed on the same day as the
present application in the name of Vision IQ and entitled "Method
and system for calculating a transformed image from a digital
image and formatted information r elated to a geometric
transformation". That application describes a method for
calculating a transformed image from a digital image and
formatted information 15 related to a geometric transformation,
especially formatted information 15 related to the disto rtions
and/or chromatic aberrations of an appliance chain P3. The method
includes the stage of calculating the transformed image from an
approximation of the geometric transformation. It results
therefrom that the calculation is economic al in terms of memory
resources, in memory bandpass, in calculating power and therefore
in electricity consumption. It also results therefrom that the
transformed image does not exhibit any visible or annoying defect
as regards its subsequent use.
To exploit the formatted i nformation 15, it is possible,
for example, to use the method and the system described in the
International Patent Application filed on the same day as the
present application in the name of Vision IQ and entitled "Method
and system for modifying a digital image, taking into account its
noise". That application describes a method for calculating a
transformed image from a digital image and formatted information
15 related to the defects P5 of an appliance chain P3. Appliance
chain P3 includes image-capture appliances and/or image -
restitution appliances. Appliance chain P3 contains at least one
appliance P25. The method includes the stage of automatically
determining the characteristic data from the formatted
information 15 and/or the digital image. It result s from the
combination of technical features that the transformed image does
not exhibit any visible or annoying defect, especially defects
related to noise, as regards its subsequent use.
Image-processing means
Referring in particular to Figs. 7 and 13 , a description
will now be given of the concept of image -processing means P1.
Within the meaning of the present invention, image -processing
means P1 are defined, for example, as image -processing software 4
and/or a component and/or an equipment item and/o r a system
capable of modifying the quality of image 103 by employing
formatted information 15 in order to produce a modified image,
such as a corrected image 71 or a corrected restitution image 97.
The modified image may be addressed to a second appliance of
appliance chain P3, distinct or not from appliance P25 , for
example, the following appliance in appliance chain P3.
The modification of image quality by image -processing means
P1 may consist, for example, in:
- suppressing or attenuating the defects P5 of one or more
appliances P25 of appliance chain P3 in image 103, and/or
- modifying image 103 to add at least one defect P5 of one
or more appliances P25 of appliance chain P3 in such a way that
the modified image resembles an image captured by applia nee or
appliances P25, and/or
- modifying image 103 to add at least one defect P5 of one
or more appliances P25 of appliance chain P3 in such a way that
the restitution of the modified image resembles an image
restituted by appliance or appliances P25, an d/or
- modifying image 103 by taking into account the formatted
information 15 related to the vision defects P5 of the eye P25 of
a human being in appliance chain P3 in such a way that
restitution of the modified image is perceived by the eye of the
human being as corrected for all or part of the defects P5.
A correction algorithm is defined as the method employed by
an image-processing means P1 to modify image quality depending on
the defect P5.
Image-processing means P1 may assume various forms
depending on the application.
Image-processing means P1 may be integrated entirely or
partly in appliance P25, as in the following examples:
- an image-capture appliance that produces modified images,
such as a digital photo appliance in which image -processing means
P1 are integrated,
an image-restitution appliance 19, which displays or
prints modified images, such as a video projector in which image -
processing means P1 are included,
- a hybrid appliance, which corrects the defects of its
elements, such as a scanner / printer / fax machine in which
image-processing means P1 are included,
- a professional image -capture appliance, which produces
modified images, such as an endoscope in which image -processing
means P1 are included.
In the case in which image -processing means P1 are
integrated in appliance P25, appliance P25 in practice corrects
its own defects P5, and the appliances P25 of appliance chain P3
can be determined by design , for example in a fax machine : a
scanner and a printer ; nevertheless, the us er is able to use only
part of the appliances P25 of appliance chain P3, for example if
the fax machine can also be used as a stand -alone printer.
Image-processing means P1 can be integrated entirely or
partly in a computer, for example in the following m anner:
- in an operating system, such as Windows or the Mac OS, in
order to modify automatically the quality of images derived from
or addressed to a plurality of appliances P25, which may vary
depending on image 103 and/or in time, examples being scanner s,
photo appliances and printers; the automatic correction may be
made, for example, when image 103 is input into the system, or
when printing is requested by the user,
- in an image-processing application, such as Photoshop™,
to modify automatically the quality of images derived from or
addressed to a plurality of appliances P25, which may vary
depending on image and/or in time, examples being scanners, photo
appliances and printers; the automatic correction may be made,
for example, when the user activa tes a filter command in
Photoshop™,
- in a photo-printing appliance (such as Photofinishing or
Minilab in English), to modify automatically the quality of
images derived from a plurality of photo appliances, which may
vary depending on the image and/or in time, examples being
disposable cameras, digital photo appliances and compact disks,
the automatic correction may take into account the photo
appliances as well as the integrated scanner and printer, and may
be applied at the moment at which the printing jobs are
initiated,
- on a server, for example on the Internet, to modify
automatically the quality of images derived from a plurality of
photo appliances, which may vary depending on the image and/or in
time, examples being disposable cameras and digita 1 photo
appliances, the automatic correction may take into account the
photo appliances as well as a printer, for example, and may be
applied at the moment at which the images 103 are recorded on the
server, or at the moment at which the printing jobs are
initiated.
In the case in which image -processing means P1 are
integrated in a computer, image -processing means P1 are for
practical purposes compatible with multiple appliances P25, and
at least one appliance P25 of appliance chain P3 may vary from
one image 103 to another.
To provide formatted information 15 in a standard format to
image-processing means P1, it is possible, for example, to
associate the formatted information 15 with image 103:
- in a file P100,
- by using identifiers of appliances P25 of appliance chain
P3, such as data in Exif format in file P100, in order to
retrieve formatted information 15 in database 22 of
characteristics.
Variable characteristic
On the basis of Fig. 13, a description will now be given of
the concept of variable characteristic P6. According to the
invention, a variable characteristic P6 is defined as a
measurable factor, which is variable from one image 103 to
another that has been captured, modified or restituted by the
same appliance P25, and which has an infl uence on defect P5 of
the image that has been captured, modified or restituted by
appliance P25, especially:
- a global variable characteristic, which is fixed for a
given image 103, an example being a characteristic of appliance
P25 at the moment of capture or restitution of the image, related
to an adjustment of the user or related to an automatic function
of appliance P25, such as the focal length,
- a local variable characteristic, which is variable within
a given image 103, an example being coordinat es x, y or rho,
theta in the image, permitting image -processing means P1 to apply
local processing that differs depending on the zone of the image.
A measurable factor which is variable from one appliance
P25 to another but which is fixed from one image 1 03 to another
that has been captured, modified or restituted by the same
appliance P25 is not generally considered to be a variable
characteristic P6. An example is the focal length for an
appliance P25 with fixed focal length.
The adjustments used as de scribed hereinabove are examples
of variable characteristics P6.
The formatted information 15 may depend on at least one
variable characteristic P6.
By variable characteristic P6 there can be understood in
particular:
- the focal length of the optical s ystem,
- the redimensioning applied to the image (digital zoom
factor: enlargement of part of the image; and/or under -sampling:
reduction of the number of pixels of the image),
- the nonlinear brightness correction, such as the gamma
correction,
the enhancement of contour, such as the level of
deblurring applied by appliance P25,
- the noise of the sensor and of the electronic unit,
- the aperture of the optical system,
- the focusing distance,
- the number of the frame on a film,
- the underexposure or overexposure,
- the sensitivity of the film or sensor,
- the type of paper used in a printer,
- the position of the center of the sensor in the image,
- the rotation of the image relative to the sensor,
- the position of a projector relative to the screen,
- the white balance used,
- the activation of a flash and/or its power,
- the exposure time,
- the sensor gain,
- the compression,
- the contrast,
- another adjustment applied by the user of appliance P25,
such as a mode of operation,
- another automatic adjustment of appliance P25,
- another measurement performed by appliance P25.
In the case of a restitution means 19, the variable
characteristic P6 can also be defined as variable restitution
characteristic.
Variable characteristic value
On the basis of Fig. 13, a description will now be given of
the concept of variable characteristic value P26. A variable
characteristic value P26 is defined as the value of variable
characteristic P6 at the moment of capture, modification or
restitution of a specified image, such value being obtained, for
example, from data in Exif format present in file P100. Image -
processing means P1 can then process or modify the quality of
image 103 as a function of variable characteristics P6, by using
formatted information 15 that depends on variable characteristics
P6 and by determining the value P2 6 of the variable
characteristics.
In the case of a restitution means 19, the value of
variable characteristic P6 can also be defined as a variable
restitution characteristic.
Measured formatted information, extended formatted information
As illustrated in Fig. 15, the formatted information 15 or
a fraction of the formatted information 15 can include measured
formatted information P101 to illustrate a raw measur ement, such
as a mathematical field related to geometric distortion defects
at a certain number of characteristic points of an array 80. As
illustrated in Fig. 15, the formatted information 15 or a
fraction of the formatted information 15 can include exten ded
formatted information P102, which can be calculated from measured
formatted information P101, for example by interpolation for real
points other than the characteristic points of array 80. In the
foregoing, it has been seen that a formatted information item 15
might depend on variable characteristics P6. According to the
invention, a combination P120 is defined as a combination
composed of variable characteristics P6 and of values P26 of
variable characteristics, an example being a combination P120
composed of the focal length, of the focusing, of the diaphragm
aperture, of the capture speed, of the aperture, etc. and of
associated values. It is difficult to imagine how the formatted
information 15 related to different combinations P120 can be
calculated, all the more so because certain characteristics of
combination P120, such as the focal length and the distance, can
vary continuously.
The invention provides for calculating the formatted
information 15 in the form of extended formatted information P102
by interpolation from measured formatted information P101 related
to a predetermined selection of combinations P120 of known
variable characteristics P6.
For example, measured formatted information P101 related to
the combination P120 of "focal length = 2, distance = 7, capture
speed = 1/100", to the combination of "focal length = 10,
distance = 7, capture speed = 1/100" and to the combination of
"focal length = 50, distance = 7, capture speed = 1/100" is used
to calculate extended formatted information P 102 that depends on
focal length as the variable characteristic P6. By means of this
extended formatted information P102, it is possible in particular
to determine formatted information related to the combination of
"focal length = 25, distance = 7 and cap ture speed = 1/100".
The measured formatted information P101 and the extended
formatted information P102 may exhibit an interpolation deviation
P121. The invention may include the stage of selecting zero or
one or more variable characteristics P6, such th at interpolation
deviation P121 for the extended formatted information P102
obtained for the variable characteristics P6 selected in this way
is smaller than a predetermined interpolation threshold. In fact,
certain variable characteristics P6 may have a s mailer influence
than others on the defect P5, and the error introduced by making
the approximation that these are constant may merely be minimum;
for example, the focusing adjustment may have merely a slight
influence on the vignetting defect, and for thi s reason may not
be part of the variable characteristics P6 selected. The variable
characteristics P6 may be selected at the moment of production of
the formatted information 15. It results from the combination of
technical features that the modification o f image quality employs
simple calculations. It also results from the combination of
technical features that the extended formatted information P102
is compact. It also results from the combination of technical
features that the eliminated variable charact eristics P6 have the
least influence on the defect P5. It results from the combination
of technical features that image quality can be modified with
specified precision by means of the formatted information 15.
In the case of a restitution means 19, the c ombination 120
may also be defined as a restitution combination.
In the case of a restitution means 19, the measured
formatted information P101 may also be defined as measured
formatted restitution information.
In the case of a restitution means 19, the extended
formatted information P102 may also be defined as extended
formatted restitution information.
In the case of a restitution means 19, the interpolation
deviations P121 may also be defined as interpolation restitution
deviations.
Parameterizable model, parameters
Referring in particular to Figs. 5, 6 and 16, a description
will now be given of the concept of parameters P9 and
parameterizable model P10. Within the meaning of the invention, a
parameterizable model P10 is defined as a mathematical mo del that
may depend on variables P6 and that may be related to one or more
defects P5 of one or more appliances P25; parameterizable
transformation model 12, parameterizable reverse transformation
model 212, parameterizable restitution transformation model 97
and parameterizable reverse restitution transformation model 297
described hereinabove are examples of parameterizable models P10;
for example, a parameterizable model P10 may be related to:
sharpness defects or blurring of a digital photo
appliance,
- geometric vignetting defects of a photo appliance which
it is hoped can be emulated,
geometric distortion defects and geometric chromatic
aberration defects of a projector,
sharpness or blurring defects of a disposable photo
appliance combined with a scanner.
The formatted information 15 related to a defect P5 of an
appliance P25 may be presented in the form of the parameters P9
of a parameterizable model P10 depending on variable
characteristics P6; by means of the parameters P9 of
parameterizable mode 1 P10, it is possible to identify a
mathematical function P16 in a set of mathematical functions,
such as multi-variable polynomials; by means of the mathematical
functions P16, it is possible to modify image quality as a
function of specified values of th e variables P6.
In such a way that image -processing means P1 can use the
parameters P9 of parameterizable transformation model P10 to
calculate the modified image, for example to calculate the
corrected intensity or the corrected restitution intensity of a
point of the image.
Referring in particular to Fig. 15, a description will now
be given of the concept of color plane P20 of a colored image
103. Image 103 can be decomposed into color planes P20 in various
ways: number of planes (1, 3 or more), precision (8 bits
unsigned, 16 bits signed, floating, etc.) and significance of the
planes (relative to a standard color space) . Image 103 can then
be decomposed in various ways into color planes P20: red color
plane composed of red pixels, green co lor plane, blue color plane
(RGB) or brightness, saturation, hue, etc.; on the other hand,
color spaces such as PIM exist, or negative pixel values are
possible in order to permit representation of subtractive colors,
which cannot be represented in positiv e RGB; finally, it is
possible to encode a pixel value on 8 bits or 16 bits, or by
using floating values. As an example of how the formatted
information 15 may be related to the color planes P20, the
sharpness defects can be characterized differently for t he planes
of red, green and blue color, to permit image -processing means P1
to correct the sharpness defect differently for each color plane
P20.
Provision of the formatted information
On the basis in particular of Figs. 8, 13, 15 and 16, a
description will now be given of an alternative embodiment of the
invention. To provide formatted information 15 in a standard
format to image-processing means PI, the system includes data -
processing means and the method includes the stage of filling in
at least one f ield 91 of the standard format with the formatted
information 15. Field 91 may then contain in particular:
- values related to the defects P5, for example in the form
of parameters P9, in such a way that image -processing means PI
can use the parameters P9 to modify image quality by taking the
defects P5 into account, and/or
- values related to the sharpness defects, for example in
the form of parameters P9, in such a way that image -processing
means P1 can use the parameters P9 to modify image quality by
taking the sharpness defects into account, and to calculate the
corrected shape or the corrected restitution shape of a point of
the image, and/or
- values related to the colorimetry defects, for example in
the form of parameters P9, in such a way that ima ge-processing
means P1 can use the parameters P9 to modify image quality by
taking the colorimetry defects into account, and to calculate the
corrected color or the corrected restitution color of a point of
the image, and/or
- values related to the geomet ric distortion defects and/or
to the geometric chromatic aberration defects, for example in the
form of parameters P9, in such a way that image -processing means
P1 can use the parameters P9 to modify image quality by taking
the geometric distortion defects and/or the geometric chromatic
aberration defects into account, and to calculate the corrected
position or the corrected restitution position of a point of the
image, and/or
- values related to the geometric vignetting defects, for
example in the form of parameters P9, in such a way that image -
processing means P1 can use the parameters P9 to modify image
quality by taking the geometric vignetting defects into account,
and to calculate the corrected intensity or the corrected
restitution intensity of a poi nt of the image, and/or
- values related to the deviations 14, and/or
- values that are functions of variable characteristics P6
depending on image 103, for example the polynomial coefficients
and terms which depend on the variable characteristic P6
corresponding to the focal length and with which it is possible
to calculate the corrected intensity of a point of the image as a
function of its distance from the center, in such a way that the
image-processing means can calculate the corrected intensity of a
point for any value of focal length of the image -capture
appliance at the moment at which image 103 was captured,
- values related to formatted information related to the
color planes P20,
- values related to formatted information,
- values related to measured formatted information,
- values related to extended formatted information.
Production of formatted information
On the basis in particular of Figs. 7, 12 and 17, a
description will now be given of an alternative embodiment of the
invention. To produce formatted information 15 related to the
defects P5 of the appliances P25 of an appliance chain P3, the
invention can employ data -processing means and the first
algorithm and/or second algorithm and/or third algorithm and/or
fourth algorithm and/or fifth algorithm and/or sixth algorithm
and/or seventh algorithm and/or eighth algorithm as described
hereinabove.
Application of the invention to cost reduction
Cost reduction is defined as a method and system for
lowering the cost of an appliance P25 o r of an appliance chain
P3, especially the cost of the optical system of an appliance or
of an appliance chain, the method consisting in :
- reducing the number of lenses, and/or
- simplifying the shape of the lenses, and/or
- designing an optical syste m having defects P5 that are
larger than those desired for the appliance or the appliance
chain, or choosing the same from a catalog, and/or
using materials, components, processing operations or
manufacturing methods that are less costly for the applian ce or
the appliance chain and that add defects P5.
The method and system according to the invention can be
used to lower the cost of an appliance or of an appliance chain:
it is possible to design a digital optical system, to produce
formatted information 15 related to the defects P5 of the
appliance or of the appliance chain, to use this formatted
information to enable image -processing means PI, whether they are
integrated or not, to modify the quality of images derived from
or addressed to the appliance or to the appliance chain, in such
a way that the combination of the appliance or the appliance
chain with the image-processing means is capable of capturing,
modifying or restituting images of the desired quality at reduced
cost.
WE CLAIM:
1. A method for providing formatted information (15), in a standard format,
to image-processing means (PI), especially to software and/or
components; the said formatted information (15) being related to the
defects (P5) of an appliance chain (P3); the said appliance chain (P3)
comprising in particular at least one image-capture appliance (1) and/or
one image-restitution appliance (19); the said image-processing means
(PI) using the said formatted information (15) to modify the quality of at
least one image (103) derived from or addressed to the said appliance
chain (P3) ;
the said formatted information (15) comprising :
data characterizing the defects (P5) of the said image-capture appliance
(1) especially the distortion characteristics ; and/or
data characterizing the defects (P5) of the said image-restitution
appliance, especially the distortion characteristics ;
the defects being comprised in the group comprising : geometric defects,
sharpness defects colorimetry defects, geometric distortion defects,
geometric chromatic abberation defects, geometric vignetting defects,
contrast defects, colorimetry defects, in particular rendering of colors and
color cast, defects of flash uniformity, sensor noise, grain, astigmatism
defects and spherical abberation defects,
the said method comprising the stage of filling in at least one field (91) of the
said standard format with the said formatted information (15); the said field
(91) being designated by a field name; and said field (91) containing at least
one field value.
2. A "method according to claim 1, the said method being such that the said
field (91) is related to the sharpness defects (P5) of the said image-capture
appliance (1) and/or of the said image-restitution appliance (19); the said
method being such that the said field (91) contains at least one value related
to the sharpness defects (P5) of the said image-capture appliance (1)
and/or of the said image-restitution appliance (19).
3. A method according to any one of claims 1 or 2; the said
method being such that the said field (91) is related to the
colorimetry defects (P5) of the said image -capture appliance (1)
and/or of the said image-restitution appliance (19); the said
method being such that the said field (91) contains at least one
value related to the colorimetry defects (P5) of the said image -
capture appliance (1) and/or of the said image -restitution
appliance (19).
4. A method according to any one of claims 1 to 3; the said
method being such that the said field (91) is related to the
geometric distortion defects (P5) and/or to the geometric
chromatic aberration defects (P5) of the said image -capture
appliance (1) and/or of the said image -restitution appliance
(19); the said method being such that the said field (91)
contains at least one value related to the geometric distortion
defects (P5) and/or to the geometric chromatic aberration defects
(P5) of the said image-capture appliance (1) and/or of the said
image-restitution appliance (19).
5. A method according to any one of claims 1 to 4; the said
method being such that the said field (91) is related to the
geometric vignetting defects (P5) and/or to the contrast defects
(P5) of the said image-capture appliance (1) and/or of the said
image-restitution appliance (19); the said method being such that
the said field (91) contains at least one value related to the
geometric vignetting defects (P5) and/or to the contrast defects
(P5) of the said image-capture appliance (1) and/or of the said
image-restitution appliance (19).
6. A method according to any one of claims 1 to 5; the said
method being such that the said field (91) contains at least one
value related to the deviations (14).
7. A method according to claim 2; the said formatted
information (15) being composed at least partly of parameters
(P9) of a parameterizable transformation model (12)
representative of the sharpness defects (P5) of the image -capture
appliance (1) and/or of the image -restitution appliance (19); the
said method being such that the said value or values contained in
the said field (91) related to the sharpness defects (P5) are
composed at least partly of parameters (P9) of the said
parameterizable transformation model (12);
in such a way that the said image -processing means (P1) can
use the said parameters (P9) of the said parameterizable
transformation model (12) to calculate the corrected shape or the
corrected restitution shape of a point of the image (103).
8. A method according to claim 3; the said formatted
information (15) being composed at least partly of parameters
(P9) of a parameterizable transformation model (12)
representative of the colorimetry defects (P5) of the image -
capture appliance (1) and/or of the image -restitution appliance
(19); the said method being such that the said value or values
contained in the said field (91) related to the colorimetry
defects (P5) are composed at least partly of parameters (P9) of
the said parameterizable transformation model (12);
in such a way that the said image -processing means (P1) can
use the said parameters (P9) of the said parameterizable
transformation model (12) to calculate the corrected color or the
corrected restitution color of a point of the image (103) .
9. A method according to claim 4; the said formatted
information (15) being composed at least partly of parameters
(P9) of a parameterizable transformation model (12)
representative of the geometric distortion defects (P5) and/or of
the geometric chromatic aberration d efects (P5) of the image -
capture appliance (1) and/or of the image -restitution appliance
(19); the said method being such that the said value or values
contained in the said field (91) related to the geometric
distortion defects (P5) and/or to the geometric chromatic
aberration defects (P5) are composed at least partly of
parameters (P9) of the said parameterizable transformation model
(12);
in such a way that the said image -processing means (P1) can
use the said parameters (P9) of the said parameterizable
transformation model (12) to calculate the corrected position or
the corrected restitution position of a point of the image (103).
10. A method according to claim 5; the said formatted
information (15) being composed at least partly of parameters
(P9) of a parameterizable transformation model (12)
representative of the geometric vignetting defects (P5) and/or of
the contrast defects (P5) of the image -capture appliance (1)
and/or of the image-restitution appliance (19); the said method
being such that the said value or values contained in the said
field (91) related to the geometric vignetting defects (P5)
and/or to the contrast defects (P5) are composed at least partly
of parameters (P9) of the said parameterizable transformation
model (12) ;
in such a way that the said image-processing means (P1) can
use the said parameters (P9) of the said parameterizable
transformation model (12) to calculate the corrected intensity or
the corrected restitution intensity of a point of the image
(103) .
11. A method according to any one of claims 1 to 10; to
provide the said formatted information (15) in a standard format
to the said image-processing means (P1), the said method
additionally includes the stage of associating the said formatted
information (15) with the said image (103) .
12. A method according to claim 11; the said image (103)
being transmitted in the form of a file (P100) ; the said file
(P100) additionally containing the said formatted information
(15) .
13. A method according to any one of claims 1 to 12; the
said image-capture appliance (1) and/or the said image -
restitution appliance (19) including at least one variable
characteristic (P6) depending on the image (103), especially the
focal length; at least one of the said defects (P5), especially the geometric
distortion defect of said image-capture appliance (1) and/or of the said image-
restitution appliance (19) depending on the said variable characteristic (P6);
the said method being such that at least one of the said fields (91) contains at
least one value that is a function of the said variable characteristic (P6)
depending on the image (103);
. in such a way that the image-processing means (P1) can process the said
image (103) as a function of the said variable characteristics (P6).
14. A method according to any one of claims 1 to 13, the said formatted
information (15) being measured formatted information (P101), at least in part.
15. A method according to any one of claims 1 to 14, the said formatted
information (15) being extended formatted information (PI02), at least in part.
16. A method according to any one of claims 1 to 15, the said image (103) being
composed of color planes (P20); the said formatted information (15) being at
least partly related to the said color planes (P20).
17. A system for providing formatted information (15), in a standard format to
image-processing means (P1), especially to software and/or components; the
said formatted information (15) being related to the defects (P5) of an appliance
chain (P3); the said appliance chain (P3) comprising in particular at least one
image-capture appliance (1) and/or one image - restitution appliance (19); the
said image-processing means (P1) using the said formatted information (15) to
modify the quality of at least one image 103 derived from or addressed to the
said appliance chain (P3) ;
the system comprising
- means for producing formatted information,
the said formatted information (15) comprising :
data characterizing the defects (P5) of the said image-capture appliance
(1) especially the distortion characteristics; and/or
data characterizing the defects (P5) of the said image-restitution
appliance (19), especially the distortion characteristics;
data-processing means for filing in at least one field (91) of the said
standard format with the said formatted information (15);
the said field (91) being designated by a field name; the said field (91)
containing at least one field value, and
- means for transmitting formatted information to the image-processing
means.
18. A system according to claim 17, the said system being such that the said
field (91) is related to the sharpness defects (P5) of the said image-capture
appliance (1) and/or of the said image-restitution appliance (19); the said
system being such that the said field (91) contains at least one value related to
the sharpness defects (P5) of the said image-capture appliance (1) and/or of the
said image-restitution appliance (19).
19. A system according to any one of claims 17 or 18; the said system being
such that the said field (91) is related to the colorimetry defects (P5) of the said
image-capture appliance (1) and/or of the said image-restitution appliance (19);
the said system being such that the said field (91) contains at least one value
related to the colorimetry defects (P5) of the said image-capture appliance (1)
and/or of the said image-restitution appliance (19).
20. A system according to any one of claims 17 to 19; the said system being
such that the said field (91) is related to the geometric distortion defects (P5)
and/or to the geometric chromatic abberation defects (P5) of the said
image-capture appliance (1) and/or of the said image-restitution appliance
(19); the said system being such that the said field (91)
contains at least one value related to the geometric distortion
defects (P5) and/or to the geometric chromatic aberration defects
(P5) of the said image-capture appliance (1) and/or of the said
image-restitution appliance (19) .
21. A system according to any one of claims 17 to 20; the
said system being such that the said field (91) is related to the
geometric vignetting defects (P5) and/or to the contrast de fects
(P5) of the said image-capture appliance (1) and/or of the said
image-restitution appliance (19); the said system being such that
the said field (91) contains at least one value related to the
geometric vignetting defects (P5) and/or to the contrast defects
(P5) of the said image-capture appliance (1) and/or of the said
image-restitution appliance (19) .
22. A system according to any one of claims 17 to 21; the
said system being such that the said field (91) contains at least
one value related to the deviations (14).
23. A system according to claim 18; the said formatted
information (15) being composed at least partly of parameters
(P9) of a parameterizable transformation model (12)
representative of the sharpness defects (P5) of the image -capture
appliance (1) and/or of the image -restitution appliance (19); the
said system being such that the said value or values contained in
the said field (91) related to the sharpness defects (P5) are
composed at least partly of parameters (P9) of the said
parameterizable transformation model (12).
24. A system according to claim 19; the said formatted
information (15) being composed at least partly of parameters
(P9) of a parameterizable transformation model (12)
representative of the colorimetry defects (P5) of th e image-
capture appliance (1) and/or of the image -restitution appliance
(19) ; the said system being such that the said value or values
contained in the said field (91) related to the colorimetry
defects (P5) are composed at least partly of parameters (P9) of
the said parameterizable transformation model (12).
25. A system according to claim 20; the said formatted
information (15) being composed at least partly of parameters
(P9) of a parameterizable transformation model (12)
representative of the geometric distortion defects (P5) and/or of
the geometric chromatic aberration defects (P5) of the image -
capture appliance (1) and/or of the image -restitution appliance
(19) ; the said system being such that the said value or values
contained in the said field (91) related to the geometric
distortion defects (P5) and/or to the geometric chromatic
aberration defects (P5) are composed at least partly of
parameters (P9) of the said parameterizable transformation model
(12) .
26. A system according to claim 21; the said formatted
information (15) being composed at least partly of parameters
(P9) of a parameterizable transformation model (12)
representative of the geometric vignetting defects (P5) and/or of
the contrast defects (P5) of the image -capture appliance (1)
and/or of the image-restitution appliance (19); the said system
being such that the said value or values contained in the said
field (91) related to the geometric vignetting defects (P5)
and/or to the contrast defects (P5) are composed at least partly
of parameters (P9) of the said parameterizable transformation
model (12).
27. A system according to any one of claims 17 to 26; to
provide the said formatted information (15) in a standard format
to the said image-processing means (P1), the said system
additionally includes data-processing means for associating the
said formatted information (15) with the said image (103).
28. A system according to claim 27; the said system
including transmission means to transmit the said image (103) in
the form of a file (P100) ; the said file (P100) additionally
containing the said formatted information (15).
29. A system according to any one of claims 17 to 28; the
said image-capture appliance (1) and/or the said image -
restitution appliance (19) including at least one variable
characteristic (P6) depending on the image (103), especially the
focal length; at least one of the said defects (P5), especially
the geometric distortion defect of the said image -capture
appliance (1) and/or of the said image -restitution appliance (19)
depending on the said variable characteristic (P6) ; the said
system being such that at least one of the said fields (91)
contains at least one value that is a function of the said
variable characteristic (P6) depending on the image (103).
30. A system according to any one of claims 17 to 29; the
said formatted information (15) being measured formatted
information (P101), at least in part.
31. A system according to any one of claims 17 to 30; the
said formatted information (15) being extended formatted
information (P102), at least in part.
32. A system according to any one of claims 17 to 31; the
said image (103) being composed of color planes (P20); the said
formatted information (15) being at least partly related to the
said color planes (P20).

The invention concerns a method for providing, in
accordance with a standard format, formatted data
(16) to image processing means (P1), in particular
software and/or components. The formatted date (15)
are related to the defects (P5) of a set of appliances (P3)
including an image capture appliance and/or an image scanning
appliance. The image processing means (P) use the formatted data
(15) to modify the quality of at least one image (103) derived from
or addressed to said chain of appliances (P3). The formatted data
(15) comprises;data characterising the defects (P5) of
said image capture appliance, in particular the distortion
characteristics; and/or data characterising the defects (P5) of said
image scanning appliance, in particular the distortion
characteristics. The method comprises a step which consists
in informing at least a field of said standard format with the
formatted data (15). Said field is given a field name. Said field
contains at least a field value.