FR2827460A1 - Image handling system formats data with sensor characteristics - Google Patents

Abstract

An image handling system adds general and instantaneous technical characteristics of the image sensor (1) and image restoration (103) equipment including geometry, vignetting, focussing and colour to create formatted data (15).

Description

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METHOD AND SYSTEM FOR PROVIDING, IN STANDARD FORMAT,
IMAGE PROCESSING SOFTWARE OF INFORMATION RELATING TO THE CHARACTERISTICS OF IMAGE CAPTURE APPARATUS AND / OR
IMAGE RESTITUTION MEANS
The present invention relates to a method and a system for providing, in a standard format, to image processing software formatted information related to the characteristics of image capture apparatus and / or image rendering means.

 With this formatted information, said image processing software can: - perform more precise calculations or transformations on the image, and / or - improve the quality of the image, and / or - allow, in devices of image capture, the use of lower quality lenses and lower cost.

Process
The invention relates to a method. Image capturing devices are used by users to capture images of scenes, including animated scenes. The image rendering means are used by users to render captured images using image capturing devices. The method comprises the step of producing:

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 data relating to the intrinsic technical characteristics of the apparatus, in particular the distortion characteristics, and / or data relating to the technical characteristics of the apparatus at the time of the image capture, in particular the exposure time.

 These data are hereinafter referred to as formatted information.

 According to an alternative embodiment, the method may further comprise the step of producing: data relating to the intrinsic technical characteristics of the image restitution means, in particular the distortion characteristics, and / or data relating to the technical characteristics of the means of restitution of the images at the moment of the restitution of the images, in particular the resolution.

 These data are hereinafter referred to as formatted information.

 The method further includes the step of populating fields with the formatted information.

 Preferably, according to the invention the method further comprises the step of producing data relating to the preferences of the user, in particular the zoom factor. The formatted information also includes the data relating to the preferences of the user.

 Preferably, according to the invention the method is such that the fields are composed of geometric distortion defects.

 Preferably, according to the invention, the method is such that the fields are composed of: - vignetting geometrical defects, and / or - dominantly colored geometrical defects, and / or - of the focal length, and / or - parameters the method of quantifying the variation of the intensity, and / or

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 parameters of the method of quantization of the exposure time, and / or of the parameters of the digital processing carried out by the image capture apparatus, in particular the digital zoom, the compression, and / or parameters representing the preferences of the user, particularly with regard to the degree of blur, the resolution of the image.

 Preferably, according to the invention, the method is such that the fields are composed of: - the sharpness of the view, and / or - the depth of field, and / or - the parameters of the method of quantifying the geometry of the sensor and / or - parameters of the method of quantification of the noise of the sensor, and / or - parameters of the method of reconstitution of the signal of the geometry of the restitution medium, and / or - parameters of the method of reconstitution of the signal of the position of the reproduction medium, and / or of the parameters of the noise signal reconstitution method of the image restitution means.

 Preferably, according to the invention, the process is such that the fields are composed of deviations.

 Preferably, according to the invention, the method is such that the geometric distortion defects are at least partly characterized by the parameters of a parameterizable transformation model representing the technical characteristics of the image-capture apparatus at the time of transmission. image capture. Parameters of the parameterizable transformation model make it possible to calculate the corrected position of a point of the image.

 Preferably, according to the invention the method is such that the geometric vignetting defects are at least partially characterized by the parameters of a model of

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 Parametric transformation representing the technical characteristics of the image capture apparatus at the time of image capture. Parameters of the parameterizable transformation model make it possible to calculate the corrected intensity of a point of the image.

 Preferably, according to the invention, the method is such that the geometric dominant color defects are at least partially characterized by the parameters of a parameterizable transformation model representing the technical characteristics of the image capture apparatus at the time of image capture. Parameters of the parameterizable transformation model make it possible to calculate the corrected color of a point of the image.

 Preferably, according to the invention, the method is such that the sharpness of the view is at least partly characterized by the parameters of a parameterizable transformation model representing the technical characteristics of the image-capturing apparatus at the time of transmission. image capture. Parameters of the parameterizable transformation model make it possible to calculate the corrected shape of a point of the image.

 Preferably, according to the invention, the method is such that the geometric distortion defects are at least partly characterized by the parameters of a parameterizable restitution transformation model representing the technical characteristics of the image restitution means at the moment of restitution. images. Parameters of the parameterizable rendering transformation model make it possible to calculate the corrected restitution position of a point of the image.

 Preferably, according to the invention, the method is such that the geometric vignetting defects are at least partially characterized by the parameters of a parameterizable rendering transformation model representing the technical characteristics of the image restitution means at the time of rendering. images. Model parameters

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 Parametric rendering transformation allows to calculate the corrected intensity of restitution of a point of the image.

 Preferably, according to the invention, the method is such that the dominant color geometrical defects are at least partially characterized by the parameters of a parameterizable restitution transformation model representing the technical characteristics of the means of restitution of the images at the moment of reproduction. restitution of the images. Parameters of the parameterizable rendering transformation model make it possible to calculate the corrected color of reproduction of a point of the image.

 Preferably, according to the invention, the method is such that the sharpness of the view is at least partly characterized by the parameters of a parameterizable restitution transformation model representing the technical characteristics of the means of restitution of the images at the time of rendering. images. Parameters of the parameterizable rendering transformation model make it possible to calculate the corrected form of restitution of a point of the image.

System
The invention also relates to a system for providing, in a standard format, to image processing software formatted information related to the characteristics of the image capture apparatus and / or image rendering means. Image capturing devices are used by users to capture images of scenes, including animated scenes. The image rendering means are used by users to render captured images using image capturing devices. The formatted information includes: data relating to the intrinsic technical characteristics of the apparatus, including distortion characteristics, and / or

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 data relating to the technical characteristics of the apparatus at the time of the image capture, in particular the exposure time, and / or data relating to the intrinsic technical characteristics of the image rendering means, in particular the distortion characteristics, and / or data relating to the technical characteristics of the means of restitution of the images at the time of the restitution of the images, in particular the resolution.

 The format includes fields filled with formatted information.

 Preferably, according to the invention, the formatted data further comprises data relating to the preferences of the user, in particular the zoom factor.

 Preferably, according to the invention, the fields are composed of geometric distortion defects.

 Preferably, according to the invention, the fields are composed of: - vignetting geometrical defects, and / or - dominantly colored geometrical defects, and / or - of the focal length, and / or - parameters of the method of quantization of the variation of the intensity, and / or - of the parameters of the method of quantization of the exposure time, and / or - the parameters of the digital treatments carried out by the image-capture apparatus, in particular the digital zoom, compression, and / or parameters representing the preferences of the user, particularly with regard to the degree of blur, the resolution of the image.

 Preferably, according to the invention, the fields are composed of: - the sharpness of the view, and / or - the depth of field, and / or

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 parameters of the method of quantification of the geometry of the sensor, and / or of the parameters of the method of quantification of the noise of the sensor, and / or of the parameters of the method of reconstitution of the signal of the geometry of the reproduction medium and / or - parameters of the signal reconstitution method of the position of the reproduction medium, and / or - parameters of the method of reconstitution of the noise signal of the image restitution means.

 Preferably, according to the invention, the fields are composed of deviations.

 Preferably, according to the invention, the format is such that the geometric distortion defects are at least partially characterized by the parameters of a parameterizable transformation model representing the technical characteristics of the image capture apparatus at the time of image capture. Parameters of the parameterizable transformation model make it possible to calculate the corrected position of a point of the image.

 Preferably, according to the invention, the format is such that the geometric vignetting defects are at least partly characterized by the parameters of a parameterizable transformation model representing the technical characteristics of the image-capture apparatus at the time of image capture. Parameters of the parameterizable transformation model make it possible to calculate the corrected intensity of a point of the image.

 Preferably, according to the invention, the format is such that the geometric dominant color defects are at least partly characterized by the parameters of a parameterizable transformation model representing the technical characteristics of the image capture apparatus at the moment. of image capture. The parameters of the transformation model

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 parametriz allow to calculate the corrected color of a point of the image.

 Preferably, according to the invention, the format is such that the sharpness of the view is at least partly characterized by the parameters of a parameterizable transformation model representing the technical characteristics of the image capture apparatus at the time of image capture. Parameters of the parameterizable transformation model make it possible to calculate the corrected shape of a point of the image.

 Preferably, according to the invention, the format is such that the geometric distortion defects are at least partially characterized by the parameters of a parameterizable restitution transformation model representing the technical characteristics of the image restitution means at the time of the reproduction. restitution of the images. Parameters of the parameterizable rendering transformation model make it possible to calculate the corrected restitution position of a point of the image.

 Preferably, according to the invention, the format is such that the geometric vignetting defects are at least partly characterized by the parameters of a parameterizable rendering transformation model representing the technical characteristics of the means of restitution of the images at the time of the reproduction. restitution of the images. Parameters of the parameterizable rendering transformation model make it possible to calculate the corrected intensity of reproduction of a point of the image.

 Preferably, according to the invention, the format is such that the geometric dominant color defects are at least partially characterized by the parameters of a parameterizable restitution transformation model representing the technical characteristics of the image restitution means at the time of printing. the restitution of the images. Parameters of the parameterizable rendering transformation model make it possible to

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 calculate the corrected color of rendition of a point of the image.

 Preferably, according to the invention, the format is such that the sharpness of the view is at least partly characterized by the parameters of a parameterizable rendering transformation model representing the technical characteristics of the means of restitution of the images at the time of presentation. restitution of the images. Parameters of the parameterizable rendering transformation model make it possible to calculate the corrected form of restitution of a point of the image.

 Nomenclature of figures and brief description thereof.

 We will now draw up the list of figures and briefly describe their contents. The definitions of the technical terms used will be specified below.

 The figures represent respectively: FIG. 1: a schematic view of an image capture, - FIG. 2: a schematic view of an image restitution, - FIG. 3: a schematic view of the pixels of an image, - FIGS. 4a and 4b: two schematic views of a reference scene, - figure 5: the flowchart of the method for calculating the difference between the mathematical image and the corrected image, - figure 6: the flowchart of the method to obtain the best rendition transformation for an image rendering means, - Figure 7: a schematic view of the elements making up the system according to the invention, - Figure 8: a schematic view of the fields of the formatted information,

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FIG. 9a is a diagrammatic front view of a mathematical point; FIG. 9b is a diagrammatic front view of a real point of an image; FIG. 9c is a schematic side view of a mathematical point; FIG. 9d: a schematic profile view of a real point of an image, FIG. 10: a schematic view of a characteristic point grid, FIG. 11: the flowchart of the method for obtaining the formatted information. FIG. 12: the flow diagram of the method making it possible to obtain the best transformation for an image capture apparatus,
FIG. 1 shows: a scene 3 comprising an object 107, a sensor 101 and the surface of the sensor 110, an optical center 111, an observation point 105 on a surface of the sensor 110, an observation direction 106 passing through the observation point 105, the optical center 111, the scene 3, a surface 10 geometrically associated with the surface of the sensor 110.

 FIG. 2 shows an image 103, an image reproduction means 19 and a restored image 191 obtained on the reproduction medium 190.

 FIG. 3 shows a scene 3, an image capture apparatus 1 and an image 103 consisting of pixels 104.

 FIGS. 4a and 4b show two variants of a reference scene 9.

 FIG. 5 shows a flow diagram implementing a scene 3, a mathematical projection 8 giving a mathematical image 70 of the scene 3, a real projection 72 giving an image 103 of the scene 3 for the characteristics used 74, a model of transformation

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 configurable 12 giving a corrected image 71 of the image 103, the corrected image 71 having a difference 73 with the mathematical image 70.

 FIG. 6 shows a flowchart implementing an image 103, a real reproduction projection 90 giving a restored image 191 of the image 103 for the restitution characteristics used 95, a parameterizable restitution transformation model 97 giving a corrected rendering image 94 of the image 103, a mathematical rendering projection 96 giving a mathematical rendering image 92 of the corrected rendering image 94 and having a rendering difference 93 with the rendered image 191.

 FIG. 7 shows a system comprising an image-capture apparatus 1 consisting of an optic 100, a sensor 101 and an electronics 102. FIG. 7 also shows a memory zone 16 containing an image 103, a database 22 containing formatted information 15, transmission means 18 of the completed image 120 consisting of the image 103 and information formatted 15 to calculation means 17 containing processing software picture 4.

 FIG. 8 shows formatted information consisting of fields 90.

 FIGS. 9a to 9d show a mathematical image 70, an image 103, the mathematical position 40 of a point, the mathematical form 41 of a point, compared to the real position 50 and the actual shape 51 of the point corresponding picture.

 In Figure 10 there is shown a grid 80 of characteristic points.

 FIG. 11 shows a flow diagram implementing an image 103, characteristics used 74, a characteristics database 22. The formatted information is obtained from the characteristics

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 used 74 and stored in the database 22. The completed image 120 is obtained from the image 103 and formatted information 15.

 FIG. 12 shows a flow diagram implementing a reference scene 9, a mathematical projection 8 giving a synthesis image class 7 of the reference scene 9, a real projection 72 giving a reference image 11 of the reference scene 9 for the characteristics used 74. This flowchart also implements a parameterizable transformation model 12 giving a transformed image 13 of the reference image 11. The transformed image 13 has a difference 14 with the image class summary 7.

Definitions and detailed description
Other characteristics and advantages of the invention will become apparent on reading: - definitions, hereinafter explained, of the technical terms employed illustrated with reference to the indicative and nonlimiting examples of FIGS. 1 to 12, - of the description of the figures 1 to 12.

Scene
Scene 3 is a place in three-dimensional space, which includes objects 107 illuminated by light sources.

Image Capture Device, Image, Image Capture
Referring to FIGS. 3 and 7, reference will now be made to what is meant by image capture apparatus 1 and image 103. An image capture apparatus 1 is an apparatus consisting of optics 100, one or more sensors 101, an electronic 102, a memory zone 16. Said image capture apparatus 1 allows from a scene 3 to obtain fixed digital images 103 or animated recorded in the memory zone 16 or transmitted to an external device. Animated images consist of a succession in time, of still images 103. Said image capture apparatus 1 can

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 take the form of a camera, a camcorder, a camera connected or integrated in a PC, a camera connected or integrated with a personal assistant, a camera connected or integrated into a phone, a videoconferencing apparatus or a camera or measuring device sensitive to wavelengths other than visible light such as a thermal camera.

 Image capture is the process of computing the image 103 by the image capture apparatus 1.

 In the case where an apparatus comprises several interchangeable subassemblies, in particular an optical 100, is called image capture apparatus 1, a particular configuration of the apparatus.

Image restitution method, Restored image, Image restitution
FIG. 2 will now describe what is meant by image-reproduction means 19. Such image-restitution means 19 may take the form of a display screen, a TV, flat screen, projector, virtual reality glasses, printer.

 Such image recovery means 19 comprises: - an electronics, - one or more sources of light, electrons or ink, - one or more modulators: light modulation devices, electrons or ink a focusing device, in particular in the form of an optical element in the case of a light projector, or in the form of electron beam focusing coils in the case of a cathode-ray tube screen, or in the form of electron beam focusing coils in the case of a cathode-ray tube screen, or the form of filters in the case of a flat screen, - a reproduction medium 190 being in particular in the form of a screen in the case of a tube screen

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 cathode, a flat screen or a projector, in the form of a printing medium on which the printing is carried out in the case of a printer, or in the form of a virtual surface in the space in the case of a virtual image projector.

 Said image restitution means 19 makes it possible, starting from an image 103, to obtain a restored image 191 on the reproduction medium 190.

 Animated images consist of a succession in time, of still images.

 Image rendering is the process of displaying or printing the image by the image rendering means 19.

 In the case where a reproduction means 19 comprises several interchangeable sub-assemblies or able to move relative to one another, in particular the reproduction medium 190, a particular configuration is called image reproduction means 19. .

Sensor Surface, Optical Center, Focal Length
We will now describe with reference to FIG. 1 what is called sensor surface 110.

 The surface of the sensor 110 is the shape in the space drawn by the sensitive surface of the sensor 101 of the image capture apparatus 1 at the time of the image capture.

This surface is generally flat.

 Optical center 111 is a point in the space associated with image 103 at the time of image capture. The focal distance is the distance between this point 111 and the plane 110, in the case where the surface of the sensor 110 is flat.

 Pixel, Pixel value, Exposure time.

 Referring to FIG. 3, what is meant by pixel 104 and pixel value will now be described.

 Pixel 104 is called an elementary zone of the surface of the sensor 110 obtained by creating a paving generally

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 regular, of said surface of the sensor 110. A pixel value is called a number associated with this pixel 104.

 An image capture consists in determining the value of each pixel 104. The set of these values constitutes the image 103.

 During an image capture, the pixel value is obtained by the integration on the surface of the pixel 104, during a period of time called exposure time, of a part of the luminous flux coming from the scene 3 through optics 100 and converting the result of this integration into a numerical value.

The integration of the luminous flux and / or the conversion of the result of this integration into a numerical value are carried out by means of the electronics 102.

 This definition of the notion of pixel value applies to the case of images 103 in black and white or in color, whether fixed or animated.

 However, depending on the case, the part of the luminous flux concerned is obtained in various ways: a) In the case of a color image 103, the surface of the sensor 110 generally comprises several types of pixels 104, respectively associated with luminous fluxes. of different wavelengths, such as for example red, green and blue pixels. b) In the case of a color image 103, there may also be several juxtaposed sensors 101 which each receive a portion of the luminous flux. c) In the case of a color image 103, the colors used may be different from red, green and blue, as for example for the American NTSC television, and may be in number greater than three. d) Finally, in the case of an interlaced scanning television camera, the animated images produced consist of an alternation of images 103 comprising the even lines, and images 103 comprising the odd lines.

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 Configuration used, Settings used, Characteristics used.

 The configuration used is the list of removable subassemblies of the image capture apparatus 1, for example the optics 100 actually mounted on the image capture apparatus 1 if it is interchangeable. The configuration used is characterized in particular by: - the type of optics 100, - the serial number of optics 100 or any other designation.

The parameters used are: - the configuration used as defined above, as well as - the value of the manual or automatic settings available in the configuration used and having an impact on the content of the image 103. These adjustments can be made by the user, including using buttons, or calculated by the image capture apparatus 1. These settings can be stored in the device, including removable media, or any device connected to the device. These settings may include focus, aperture, and focal length settings for Optics 100, exposure time settings, white balance settings, built-in image processing settings such as digital zoom, compression, contrast,
The characteristics used 74 or set of characteristics used 74 are called: a) Parameters related to the intrinsic technical characteristics of the image capture apparatus 1, determined at the time of the image capture apparatus design. for example, these parameters may include the formula of the optics 100 of the configuration used impacting the geometrical defects and the sharpness of the captured images; the formula of the optics 100 of the configuration used includes in particular the

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 shape, arrangement and material of the lenses of optics 100.

 These parameters may furthermore comprise: the geometry of the sensor 101, namely the surface of the sensor 110 as well as the shape and the relative arrangement of the pixels 104 on this surface; the noise generated by the electronics 102; conversion of luminous flux to pixel value. b) Parameters related to the intrinsic technical characteristics of the image-capture apparatus 1, determined at the time of manufacture of the image-capture apparatus 1, and in particular: the exact positioning of the lenses in the optics 100% of the configuration used, - the exact positioning of the optics 100 with respect to the sensor 101. c) Parameters related to the technical characteristics of the image capture apparatus 1, determined at the moment of image capture 103 and in particular: the position and the orientation of the surface of the sensor 110 with respect to the scene 3, the settings used, the external factors, such as the temperature, if they have an influence. d) User preferences, including the color temperature to be used for rendering images.

These preferences are for example selected by the user with the help of buttons.

Observation point, observation direction
Referring to FIG. 1, what is meant by observation point 105 and observation direction 106 will now be described.

 A mathematical surface 10 is a surface geometrically associated with the surface of the sensor 110.

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 for example, if the surface of the sensor is flat, the mathematical surface 10 can be confused with that of the sensor.

 An observation direction 106 is a straight line passing through at least one point in scene 3 and through optical center 111. Point 105 is called the intersection of observation direction 106 and surface 10.

Observed color, Observed intensity
Referring to FIG. 1, what is meant by observed color and observed intensity will now be described.

The observed color is the color of the light emitted, transmitted or reflected by said scene 3 in said observation direction 106 at a given instant, and observed from said observation point 105. The intensity of the light is called observed intensity. transmitted by said scene 3 in said observation direction 106 at the same time, and observed from said observation point 105.

 The color may in particular be characterized by a luminous intensity depending on a wavelength, or by two values as measured by a colorimeter.

The intensity can be characterized by a value as measured with a photometer.

 Said observed color and said observed intensity depend in particular on the relative position of the objects 107 in the scene 3 and the light sources present as well as the transparency and reflection characteristics of the objects 107 at the moment of the observation.

 Mathematical Projection, Mathematical Image, Mathematical Point, Mathematical Color of a Point, Mathematical Intensity of a Point, Mathematical Form of a Point, Mathematical Position of a Point.

 The following will be described with reference in particular to FIGS. 1,5, 9a, 9b, 9c and 9d, the notions of mathematical projection 8, mathematical image 70, mathematical point, mathematical color of a point, mathematical intensity of a

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 point, mathematical form 41 of a point, mathematical position 40 of a point.

 FIG. 5 will now describe how a mathematical image 70 is produced by a determined mathematical projection 8 of at least one scene 3 on the mathematical surface 10.

 Beforehand, we will describe what we mean by mathematical projection determined 8.

 A determined mathematical projection 8 associates: - with a scene 3 at the time of the capture of an image 103, - and with the characteristics used 74, a mathematical image 70.

 A determined mathematical projection 8 is a transformation that makes it possible to determine the characteristics of each point of the mathematical image 70 from the scene 3 at the time of the image capture and the characteristics used 74.

 Preferably, the mathematical projection 8 is defined in the manner that will be described below.

 The position of the observation point 105 on the mathematical surface 10 is called the mathematical position 40 of the point.

 The mathematical form 41 of the point is called the point-like geometrical form of the observation point 105.

 The color of the point is the color observed.

 The observed intensity is called the mathematical intensity of the point.

 Mathematical point is the combination of mathematical position 40, mathematical form 41, mathematical color and mathematical intensity for the point of observation 105 considered. The mathematical image 70 consists of all of said mathematical points.

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 The mathematical projection 8 of scene 3 is the mathematical image 70.

Real Projection, Real Point, Real Color of a Point, Actual Intensity of a Point, Actual Shape of a Point, Real Position of a Point
The notions of real projection 72, real point, real color of a point, real intensity of a point, actual shape are described below with reference to FIGS. 3,5, 9a, 9b, 9c and 9d. one point, actual position 50 of a point.

 During an image capture, the image capture apparatus 1 associated with the characteristics used 74 produces an image 103 of the scene 3. The light coming from the scene 3 in an observation direction 106 passes through the optical 100 and arrives on the surface of the sensor 110.

 For the said direction of observation, a so-called real point is obtained which has differences with respect to the mathematical point.

 Referring to Figures 9a to 9d, the differences between the real point and the mathematical point will now be described.

 The actual shape 51 associated with said viewing direction 106 is not a point on the surface of the sensor, but has a three-dimensional space cloud shape, which intersects with one or more pixels 104. In particular, the differences are due to coma, spherical aberration, astigmatism, pixel clustering 104, chromatic aberration, depth of field, diffraction, spurious reflections, the field curvature of the camera. image capture 1. They give an impression of blur, lack of sharpness of the image 103.

 In addition, the actual position 50 associated with said viewing direction 106 has a difference from the mathematical position 40 of a point. This difference is due in particular to the geometric distortion, which gives

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 an impression of deformation: for example, the vertical walls look curved. It is also due to the fact that the number of pixels 104 is limited and therefore the actual position 50 can only take a finite number of values.

 In addition, the actual intensity associated with said observation direction 106 has differences with respect to the mathematical intensity of a point. These differences are due in particular to gamma and vignetting: for example the edges of the image 103 appear darker. In addition noise may be added to the signal.

 Finally, the actual color associated with said observation direction 106 has differences with respect to the mathematical color of a point. These differences have in particular originated gamma and the dominant color. In addition noise may be added to the signal.

 The real point is the association of the actual position 50, the actual shape 51, the actual color and the actual intensity for the observed direction of observation 106.

 The actual projection 72 of the scene 3 is constituted by the set of real points.

Parametric Transformation Model, Parameters, Corrected Image
Parametric transformation model 12 (or condensed, parameterizable transformation 12) is called a mathematical transformation that makes it possible to obtain, from an image 103, and from the parameter value, a corrected image 71.

In particular, said parameters can be calculated from the characteristics used as indicated below.

 Said transformation makes it possible in particular to determine for each real point of the image 103, the corrected position of said real point, the corrected color of said real point, the corrected intensity of said real point, the corrected form of said real point, starting from the value of the parameters, the actual position of the real point and the pixel values of the image

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 103. The corrected position can for example be calculated using polynomials of degree fixed 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 pixel values, the coefficients depending on the value of the parameters and the real position, or else nonlinear functions of the values of the pixels of the image 103.

 The parameters may include in particular: the focal length of the optics 100 of the configuration used or a linked value such as the position of a lens group, the focusing of the optics 100 of the configuration used or a linked value such as the position of a lens group, opening optics 100 of the configuration used or a bound 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 the mathematical image 70 and the corrected image 71 for a given scene 3 and the characteristics used 74 given, one or more values determined from the numbers characterizing the position, the color, intensity, the shape of all or part of the corrected points and all or part of the mathematical points.

 For example, the difference 73 between the mathematical image 70 and the corrected image 71 for a given scene 3 and the characteristics used 74 data can be determined in the following way: - One chooses characteristic points which can be for example the points an orthogonal grid 80 of points arranged regularly as shown in FIG.

 The difference 73 is calculated for example by performing the sum for each characteristic point of the absolute values of the differences between each number characterizing the position, the color, the intensity, the shape respectively for the point.

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 corrected and for the mathematical point. The sum function of the absolute values of the differences can be replaced by another function such as the mean, the sum of the squares or any other function allowing to combine the numbers.

Reference scene
A reference scene 9 is called a scene 3 of which certain characteristics are known. For example, Figure 4a shows a reference scene 9 consisting of a sheet of paper with circles filled with black and arranged regularly. Figure 4b shows another sheet of paper with the same circles to which are added lines and colored surfaces. The circles are used to measure the actual position 50 of a point, the lines the actual shape of a point, the colored surfaces the actual color of a point, and the actual intensity of a point. This reference scene 9 may be made of a material other than paper.

Reference image
Referring to FIG. 12, the notion of reference image 11 will now be defined. Reference image 11 is an image of reference scene 9 obtained with image capture apparatus 1.

Computer generated image, Class of computer generated images
Referring to FIG. 12, the notion of a synthetic image and a class of synthetic images 7 will now be defined. A synthesis image is a mathematical image 70 obtained by mathematical projection 8 of a reference scene. 9. A class of synthetic images 7 is 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 used 74. In the case where there is only one reference scene 9 and a set of characteristics used 74, the synthetic image class 7 includes only a synthetic image.

 Transformed image

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Referring to FIG. 12, the notion of transformed image 13 will now be defined. Transformed image 13 is the corrected image obtained by applying a parameterizable transformation model 12 to a reference image 11.

 Image transformed close to a class of computer-generated images, Difference.

 We will now describe, with reference to FIG. 12, the notion of transformed image 13 close to a class of synthetic images 7 and the notion of deviation 14.

 The difference between a transformed image 13 and a class of synthetic images 7 is defined as the smallest difference between said transformed image 13 and any of the synthetic images of said class of synthetic images.

 Next, we will describe how one chooses among the parameterizable transformation models 12 that which makes it possible to transform each reference image 11 into a transformed image 13 close to the class of synthetic images 7 of the reference scene 9 corresponding to said image of reference 11, and in different cases of reference scenes 9 and characteristics used 74.

 In the case of a given reference scene 9 associated with a set of characteristics used 74 data, the parameterizable transformation 12 (and its parameters) is chosen which makes it possible to transform the reference image 11 into the transformed image 13 which shows the smallest difference with the class of synthetic images 7. The synthesis image class 7 and the transformed image 13 are then said to be close. We call gap 14 said difference.

 In the case of a group of given reference scenes associated with given sets of characteristics 74, 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 CG class 7 of each reference scene 9

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 considered. We choose the parameterizable transformation 12 (and its parameters) which makes it possible to transform the reference images 11 into transformed images 13 such that the sum of said differences is the smallest. The sum function can be replaced by another function such as the product. The synthesis image class 7 and the transformed images 13 are then said to be close. The difference 14 is a value obtained from said differences, for example by calculating the average thereof.

 In the case where certain characteristics used are unknown, it is possible to determine them from the capture of several reference images 11 of at least one reference scene 9. In this case, the unknown characteristics are determined simultaneously. the parameterizable transformation 12 (and its parameters) which makes it possible to transform the reference images 11 into transformed images 13 such that the sum of said differences is the smallest, in particular by iterative calculation or by solving equations concerning the sum of said differences and / or their product and / or any other appropriate combination of said differences. The synthesis image class 7 and the transformed images 13 are then said to be close. The unknown characteristics can for example be the relative positions and orientations of the surface of the sensor 110 and of each reference scene 9 considered. The difference 14 is a value obtained from said differences, for example by calculating the average thereof.

Better transformation
The transformation which, among the parameterizable transformation models 12, makes it possible to transform each reference image 11 into a transformed image 13 close to the class of synthetic images 7 of the reference scene 9 corresponding to said image of reference 11.

Calibration
Calibration is a process that provides data relating to the intrinsic characteristics of

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 the image capture apparatus 1, for one or more configurations used each consisting of an optical 100 associated with an image capture apparatus 1.

 Case 1: in the case where there is only one configuration, said method comprises the following steps: - the step of mounting said optics 100 on said image-capture apparatus 1, - the step of choosing a or more reference scenes 9, - the step of choosing several characteristics used 74, - the step of capturing images of said reference scenes 9 for said characteristics used, - the step of calculating the best transformation for each group of scenes reference number 9 corresponding to the same characteristics used 74.

 Case 2: in the case where all the configurations corresponding to a given image-capturing apparatus 1 and to all the optics 100 of the same type are considered, said method comprises the following steps: the step of choosing one or several reference scenes 9, the step of choosing several characteristics used 74, the step of calculating images 103 from the characteristics used 74 and in particular the formulas of the optics 100 of the configuration used and the values of parameters. , for example using a ray tracing optical calculation software, - the step of calculating the best transformation for each group of reference scenes 9 corresponding to the same characteristics used.

 Case 3: in the case where we consider all the configurations corresponding to a given optical 100 and to all

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 1 of the same type of image capture apparatus, said method comprises the following steps: the step of mounting said optics 100 on an image capture apparatus 1 of the type considered, the step of choosing a or more reference scenes 9, - the step of choosing several characteristics used 74, - the step of capturing images of said reference scenes 9 for said characteristics used, - the step of calculating the best transformation for each group of scenes reference 9 corresponding to the same characteristics used.

 Calibration can be carried out, preferably, by the manufacturer of the image capture apparatus 1, for each device and configuration in case 1. This method is more precise but more restrictive and well suited in the case where optics 100 is not interchangeable.

 Calibration can alternatively be performed by the manufacturer of the image capture apparatus 1, for each type and configuration of apparatus in case 2. This method is less accurate but simpler.

 Calibration can alternatively be performed by the manufacturer of the image capture apparatus 1, for each optical 100 and device type in case 3. This method is a compromise allowing to use an optical 100 on all image capture apparatus 1 of a type without re-calibration for each combination of image capture apparatus 1 and optical 100.

 Calibration can alternatively be performed by the dealer or installer of the device, for each image capture device 1 and configuration in case 1.

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 Calibration can alternatively be performed by the dealer or installer of the device, for each optical 100 and type of device in case 3.

 Calibration can alternatively be performed by the user of the device for each device and configuration in case 1.

 Calibration can alternatively be performed by the user of the device, for each optical 100 and type of device in case 3.

Digital optical design
Digital optics design is a process for reducing the cost of optics 100, consisting of: designing or choosing from a catalog an optic 100 having defects, in particular for positioning the real points, reducing the number of lenses and / or - simplifying the shape of the lenses, and / or - using less expensive materials, processes or manufacturing processes.

 Said method comprises the following steps: the step of choosing an acceptable difference (in the above-defined sense), the step of choosing one or more reference scenes, and the step of choosing several characteristics used.

 Said method further comprises the iteration of the following steps: the step of choosing an optical formula comprising in particular the shape, the material and the arrangement of the lenses; the step of calculating images 103 from the characteristics used; in particular formulas of the optics 100 of the configuration used, by implementing, for example, ray tracing optical calculation software, or by taking measurements on a prototype,

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 - The step of calculating the best transformation for each group of reference scenes 9 corresponding to the same characteristics used 74, - the step of checking whether the difference is acceptable, until the difference is acceptable.

Formatted information
Formatted information associated with image 103 or formatted information 15 is referred to as all or part of the following data: data relating to the intrinsic technical characteristics of image capture apparatus 1, including distortion characteristics, and / or data relating to the technical characteristics of the image capture apparatus 1 at the time of the image capture, in particular the exposure time, and / or data relating to the preferences of said user, in particular the color temperature, and / or - discrepancy data 14.

Features Database
Characteristic database 22 is a database comprising, for one or more image capture apparatuses 1 and for one or more images 103, formatted information 15.

 Said database of characteristics 22 can be stored in a centralized or distributed manner, and can be notably: embedded in the image capture apparatus 1, integrated in the optical unit 100, integrated on a removable storage device , - integrated in a PC or other computer connected to other elements during image capture, - integrated into a PC or other computer connected to other elements after image capture,

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 - integrated in a PC or other computer capable of reading a common storage medium with the image capture apparatus 1, - integrated in a remote server connected to a PC or other computer itself connected to the other elements of the capture image.

Champs
The notion of fields 90 will now be defined with reference to FIG. 8. The formatted information associated with the image 103 can be recorded in several forms and structured in one or more tables but they logically correspond to all or some of the fields. 90, comprising: (a) focal length, (b) depth of field, (c) geometric defects.

 Said geometric defects include the image geometry defects 103 characterized by the parameters associated with the shooting characteristics 74 and a parameterizable transformation representing the characteristics of the image capture apparatus 1 at the time of shooting. .

Said parameters and said parameterizable transformation make it possible to calculate the corrected position of a point of the image 103.

 Said geometrical defects also comprise the vignetting characterized by the parameters associated with the characteristics of the shot 74 and a parameterizable transformation representing the characteristics of the image-capture apparatus 1 at the time of shooting. Said parameters and said parameterizable transformation make it possible to calculate the corrected intensity of a point of the image 103.

 Said geometrical defects furthermore comprise the colored dominant characterized by the parameters associated with the characteristics of the shot 74 and a parameterizable transformation representing the characteristics of the image-capture apparatus 1 at the time of shooting. said

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 parameters and said parameterizable transformation make it possible to calculate the corrected color of a point of the image 103.

 Said fields 90 furthermore comprise (d) the sharpness of the image 103.

 Said sharpness comprises the image resolution blur 103 characterized by the parameters associated with the shooting characteristics 74 and a parameterizable transformation representing the characteristics of the image capture apparatus 1 at the time of shooting. Said parameters and said parameterizable transformation make it possible to calculate the corrected shape of a point of the image 103. The blur covers in particular the coma, the spherical aberration, the astigmatism, the grouping in pixels 104, the chromatic aberration, depth of field, diffraction, parasitic reflections, curvature of field.

 Said dive further includes the depth of field blur, including spherical aberrations, coma, astigmatism. Said blur depends on the distance of the points of the scene 3 with respect to the image-capture apparatus 1 and is characterized by the parameters associated with the characteristics of the shot 74 and a parameterizable transformation representing the characteristics of the camera image capture 1 at the time of shooting. Said parameters and said parameterizable transformation make it possible to calculate the corrected shape of a point of the image 103.

 The said fields 90 further comprise (e) parameters of the quantization method. Said parameters depend on the geometry and physics of the sensor 101, the architecture of the electronics 102 and any processing software.

 Said parameters comprise a function representing the variations of the intensity of a pixel 104 as a function of the wavelength and the luminous flux originating from said scene 3. Said function notably comprises the gamma information.

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 Said parameters furthermore comprise: the geometry of said sensor 101, in particular the shape, the relative position and the number of the sensitive elements of said sensor 101, a function representing the spatial and temporal distribution of the noise of the image-capture apparatus 1, - a value representing the exposure time of the image capture.

 The said fields 90 furthermore comprise (f) parameters of the digital processing carried out by the image-capture apparatus 1, in particular the digital zoom, the compression. These settings depend on the processing software of the image capture apparatus 1 and the user settings.

 Said fields 90 furthermore comprise: (g) parameters representing the preferences of the user, in particular as regards the degree of blur, the resolution of the image 103.

 (h) differences 14.

Calculation of formatted information
The formatted information can be calculated and stored in the database 22 in several steps. a) A step after the design of the image capture device 1.

 This step makes it possible to obtain intrinsic technical characteristics of the image-capture apparatus 1, and in particular: the spatial and temporal distribution of the noise generated by the electronics 102; the law of conversion luminous flux in pixel value - The geometry of the sensor 101. b) A step after calibration or digital optical design.

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 This step makes it possible to obtain other intrinsic technical characteristics of the image capture apparatus 1, and in particular, for a certain number of characteristic values used, the best associated transformation and the associated difference 14. c) A step of choosing the preferences of the user using buttons, menus or removable media or connection to another device. d) An image capture step.

 This step (d) makes it possible to obtain technical characteristics of the image capture apparatus 1 at the time of the image capture, and in particular the exposure time, determined by the manual or automatic adjustments made.

 Step (d) also makes it possible to obtain the focal length. The focal length is calculated from: a measurement of the position of the group of lenses of variable focal length of the optics 100 of the configuration used, or of a given instruction to the positioning motor, or of a constructor data if the focal length is fixed.

 Said focal distance can finally be determined by analyzing the content of the image 103.

 Step (d) also makes it possible to obtain the depth of field. The depth of field is calculated from: a measurement of the position of the lens group of the optics 100 of the configuration used, or of a given instruction to the positioning motor, or of a constructor data if the depth of field is fixed.

 Step (d) also makes it possible to obtain geometry and sharpness defects. The geometry and sharpness defects correspond to a transformation calculated using a

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 combination of the transformations of the feature database 22 obtained at the end of step (b). This combination is chosen to represent the parameter values corresponding to the characteristics used 74, in particular the focal length.

 Step (d) further provides the digital processing parameters performed by the image capture apparatus 1. These parameters are determined by manual or automatic adjustments made.

 The computation of the information formatted according to steps (a) to (d) can be achieved by: - a device or software integrated in the image capture apparatus 1, and / or - a pilot software in a PC or other computer, and / or - software in a PC or other computer, and / or - a combination of all three.

 The transformations mentioned above in step (b) and in step (d) can be stored in the form of: - a general mathematical formula, - a mathematical formula for each point, - a mathematical formula for some characteristic points.

 The mathematical formulas can be described by: - a list of coefficients, - a list of coefficients and coordinates.

 These different methods make it possible to make a compromise between the size of the memory available for storing the formulas and the computing power available to calculate the corrected images 71.

 In addition, in order to retrieve the data, identifiers associated with the data are recorded in the database 22. These identifiers comprise in particular: an identifier of the type and the reference of the image capture apparatus 1,

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 an identifier of the type and of the reference of the optics 100 if it is removable, an identifier of the type and the reference of any other removable element having a link with the stored information, an identifier of the image 103, an identifier of the formatted information 15.

Completed image
As described in FIG. 11, the image 103 associated with the formatted information 15 is called the completed image 120. This completed image 120 can take the form, preferably, of a file. The completed image 120 can also be divided into several files.

 The completed image 120 may be calculated by the image capture apparatus 1. It may also be calculated by an external computing device, for example a computer.

- d'améliorer la qualité subjective des images, - de détecter des objets ou personnes 107 dans une scène 3, - d'ajouter des objets ou personnes 107 dans une scène 3, - de remplacer ou modifier des objets ou personnes 107 dans une scène 3, - de retirer les ombres d'une scène 3, Image processing software
Image processing software 4 is software that takes one or more completed images 120 as input and performs processing on these images. These treatments may include, in particular: - calculating a corrected image 71, - performing measurements in the real world, - combining several images, - improving the fidelity of images in relation to the real world,

- to improve the subjective quality of the images, - to detect objects or persons 107 in a scene 3, - to add objects or persons 107 in a scene 3, - to replace or modify objects or persons 107 in a scene 3, - to remove the shadows of a scene 3,

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 - to add shadows in a scene 3, - to search for objects in an image database.

 Said image processing software can be: - integrated with the image capture apparatus 1, - executed on calculation means 17 connected to the image-capture apparatus 1 by transmission means 18.

Digital optics
Digital optics is the combination of an image capture apparatus 1, a feature database 22 and a calculation means 17 for: image capture of an image 103; computation of the completed image, - calculation of the corrected image 71.

 Preferably, the user directly obtains the corrected image 71. If he wishes, the user can request the deletion of the automatic correction.

 The feature database 22 may be: - integrated in the image capture apparatus 1, - integrated in a PC or other computer connected to the other elements during the image capture, - integrated in a PC or other computer connected to the other elements after the image capture, - integrated in a PC or other computer capable of reading a common storage medium with the image capture apparatus 1, - integrated in a remote server connected to a PC or other computer itself connected to other elements of image capture.

 The calculation means 17 may be: integrated on a component with the sensor 101, integrated on a component with a part of the electronics 102, integrated in the image-capture device 1,

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 - integrated in a PC or other computer connected to other elements during image capture, - integrated in a PC or other computer connected to other elements after image capture, - integrated in a PC or other computer capable of reading a common storage medium with the image capture apparatus 1, - integrated in a remote server connected to a PC or other computer itself connected to the other elements of the image capture.

Treatment of the complete chain
In the preceding paragraphs, the concepts and the description of the method and system according to the invention have essentially been clarified to provide image processing software 4 with formatted information related to the characteristics of the image capture apparatus 1.

In the following paragraphs, the definition of the concepts will be expanded and the description of the method and the system according to the invention will be added to provide image processing software 4 with formatted information related to the characteristics of the reproduction means. image 19. The treatment of a complete chain will thus have been exposed.

The processing of the complete chain makes it possible: to improve the quality of the image 103 from one end of the chain to the other, to obtain a restored image 191 correcting the defects of the image capture apparatus 1 and image rendering means 19, and / or - using lower quality and lower cost optics in a video projector in combination with image quality enhancement software.

 Definitions related to image rendering means.

 Referring to FIGS. 2 and 6, consideration will now be given in the formatted information to the characteristics of an image rendering means 19 such as a printer, a display screen or a projector.

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 The additions or modifications to be made to the definitions in the case of an image restitution means 19 may be extrapolated mutatis mutandis by a person skilled in the art from the definitions provided in the case of an image capture apparatus 1. However, to illustrate this method, will now be described with particular reference to Figure 6 the main complements or modifications.

 By rendering characteristics used, the intrinsic characteristics of the image-reproduction means 19, the characteristics of the image-rendering means 19 at the time of the image rendering, and the preferences of the user at the time of the reproduction are denoted by the rendering characteristics used. restitution of the images. Especially in the case of a projector, the rendering characteristics used 95 include the shape and position of the screen used.

 The expression parameterizable transformation model 97 (or condensed, parameterizable restitution transformation 97) denotes a mathematical transformation similar to the parameterizable transformation model 12.

 The corrected restitution image 94 denotes the image obtained by applying the parameterizable restitution transformation 97 to the image 103.

 Mathematical projection projection 96 denotes a mathematical projection that associates with a corrected restitution image 94, a mathematical rendering image 92 on the mathematical rendering surface geometrically associated with the surface of the reproduction medium 190. The mathematical points of restitution of the mathematical rendering surface have a shape, position, color and intensity calculated from the corrected rendering image 94.

 Real projection projection 90 is a projection associating an image 103 with a restored image 191.

The pixel values of the image 103 are converted by the electronics of the reproduction means 19 into a signal which drives the modulator of the reproduction means 19.

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 actual points of restitution on the reproduction medium 190. The said actual points of restitution are provided with a shape, color, intensity and position. The phenomenon of pixel grouping 104 previously described in the case of an image capture apparatus 1 does not occur in the case of an image restitution means. On the other hand, an opposite phenomenon occurs which makes appear in particular lines like steps of staircase.

 The difference between the restored image 191 and the mathematical rendering image 92 is designated by the difference of restitution 93. This difference of restitution 93 is obtained mutatis mutandis as the difference 73.

 A restitution reference denotes an image 103 whose pixel values 104 are known.

 By better rendering transformation for a restitution reference and used restitution characteristics 95, the one that makes it possible to transform the image 103 into a corrected restitution image 94 such that its mathematical restitution projection 92 has the smallest difference restitution 93 with the rendered image 191.

- l'étape de choisir une référence de restitution - l'étape d'effectuer la restitution de ladite référence de restitution ; - l'étape de calculer la meilleure transformation de restitution. The rendering calibration and digital optical rendering process methods are comparable to digital optical design and calibration methods in the case of an image capture apparatus 1. However, there are differences in some steps, and in particular the following steps:

the step of choosing a restitution reference; the step of effecting the restitution of said restitution reference; the step of calculating the best rendition transformation.

 The formatted information related to an image capture apparatus 1 and those related to an image rendering means 19 may be put end to end for the same image.

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 The notion of field has previously been described in the case of an image capture apparatus 1. This notion also applies mutatis mutandis in the case of image reproduction means 19. However, to the parameters of the method of Quantization is substituted for the parameters of the signal reconstitution method, namely: the geometry of the reproduction medium 190 and its position, a function representing the spatial and temporal distribution of the noise of the image restitution means 19.

Generalization of concepts
The technical features composing the invention and appearing in the claims have been defined, described, illustrated with reference essentially to digital-type image capturing apparatus, that is to say producing digital images. It is easy to see that the same technical features apply in the case of image capture apparatus which would be the combination of a film camera (a film or film camera using silver, negative or invertible sensitive films) and a scanner producing a digital image from the developed sensitive films. Admittedly, it is appropriate in this case to adapt at least some of the definitions used. These adaptations are within the reach of the skilled person. In order to demonstrate the obviousness of such adaptations, it will be limited to mention that the notions of pixel and pixel value illustrated with reference to FIG. 3 must, in the case of the combination of a film apparatus and a scanner, apply to a basic area of the film surface after it has been scanned by means of the scanner. Such transpositions of definitions are self-evident and can be extended to the concept of configuration used. To the list of removable subassemblies of the image-capturing apparatus 1 composing the configuration used, it is possible, for example, to add the type of photographic film actually used in the film apparatus.

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NOMENCLATURE HPTCM FORMAT

<Tb>
<tb> Group <SEP> nominal <SEP> Ref. <SEP> Num.
<Tb>

<SEP><SEP> Device of <SEP><SEP> Image Capture <SEP> 1
<tb> Scene <SEP> (s) <SEP> 3
<tb> Software <SEP> (s) <SEP> of <SEP> image <SEP> processing <SEP> 4
<tb><SEP> Model (s) <SEP> of <SEP><SEP> Transformation Parameterizable <SEP> (s) <SEP> 12
<tb> Variation <SEP> (s) <SEP> 14
<tb> Formatted <SEP> Information <SEP> 15
<tb> Means <SEP> of <SEP> rendering <SEP> 19
<tb> Field <SEP> (s) <SEP> 90
<tb><SEP> (s) <SEP> model of <SEP><SEP> transformable <SEP> (s) <SEP> transformation of <SEP> 97
<tb> restitution
<tb> Image <SEP> (s) <SEP> 103
<Tb>

Claims (28)

A method for providing, in a standard format, to image processing software (4) formatted information (15) related to the characteristics of the image capturing apparatus (1) and / or the means for rendering the image image (19); said image capturing devices (1) being used by users to capture images of scenes (3), including animated scenes; said image rendering means (19) being used by users to render captured images (103) by image capture apparatus (1); said method comprising the steps of: - the step of producing: data relating to the intrinsic technical characteristics of the image capturing apparatus (1), in particular the distortion characteristics, and / or e data relating to the technical characteristics the image capturing apparatus (1) at the time of image capture, including the exposure time; said data being hereinafter referred to as the formatted information (15), and / or - the step of producing: data relating to the intrinsic technical characteristics of the image rendering means (19), in particular the distortion characteristics, and / or e data relating to the technical characteristics of the means of restitution of the images (19) at the time of the restitution of the images (103), in particular the resolution; said data being hereinafter referred to as the formatted information (15);

 said method further comprising: - the step of filling fields (90) with said formatted information (15. <Desc / Clms Page number 43>  2. Method according to claim 1; said method further comprising the step of generating data relating to said user's preferences, including the zoom factor; said formatted information (15) also including the data relating to the preferences of the user.  3. Method according to any one of claims 1 or 2; said method being such that said fields (90) are composed of: - geometric distortion defects.  4. Method according to any one of claims 1 to 3; said method being such that said fields (90) are composed of: - vignetting geometrical defects, and / or - geometric dominant color defects, and / or - of the focal length, and / or - parameters of the method of quantifying the variation of the intensity, and / or - the parameters of the method of quantizing the exposure time, and / or - the parameters of the digital processing carried out by the image-capturing apparatus (1), in particular the digital zoom, compression, and / or parameters representing the user's preferences, especially with regard to the degree of blur, the resolution of the image.  5. Method according to any one of claims 1 to 4; said method being such that said fields (90) are composed of: - the sharpness of the view, and / or - the depth of field, and / or - the parameters of the method of quantifying the geometry of the sensor, and / or - parameters of the method of quantification of the noise of the sensor, and / or <Desc / Clms Page number 44>  - parameters of the signal recovery method of the support medium geometry, and / or - parameters of the signal recovery method of the position of the recovery medium, and / or - parameters of the recovery method the noise signal of the image restitution means.  6. Process according to any one of claims 1 to 5; said method being such that said fields (90) are composed of: - gaps (14).  7. The method of claim 3; said method being such that said geometric distortion defects are at least partly characterized by parameters of a parameterizable transformation model (12) representing the technical characteristics of the image capture apparatus (1) at the time of capture image; said parameters of said parameterizable transformation model (12) for calculating the corrected position of a point of the image (103).  8. The method of claim 4; said method being such that said geometric vignetting defects are at least partly characterized by the parameters of a parameterizable transformation model (12) representing the technical characteristics of the image capture apparatus (1) at the time of capture image; said parameters of said parameterizable transformation model (12) for calculating the corrected intensity of a point of the image (103).  9. The method of claim 4; said method being such that said geometric dominant color defects are at least partly characterized by the parameters of a parameterizable transformation model (12) representing the technical characteristics of the image capture apparatus (1) at the time of the image capture; <Desc / Clms Page number 45>  said parameters of said parameterizable transformation model (12) for calculating the corrected color of a point of the image (103).  10. The method of claim 5; said method being such that said dive of the view is at least partly characterized by the parameters of a parameterizable transformation model (12) representing the technical characteristics of the image capture apparatus (1) at the time of capture image; said parameters of said parameterizable transformation model (12) for calculating the corrected shape of a point of the image (103).  11. The method of claim 3; said method being such that said geometric distortion defects are at least partly characterized by the parameters of a parameterizable rendering transformation model (97) representing the technical characteristics of the image rendering means (19) at the time of rendering the images. images; said parameters of said parameterizable rendering transformation model (97) for calculating the corrected restitution position of a point of the image (103).  12. The method of claim 4; said method being such that said geometric vignetting defects are at least partly characterized by the parameters of a parameterizable rendering transformation model (97) representing the technical characteristics of the image rendering means (19) at the time of rendering the images. images; said parameters of said parameterizable rendering transformation model (97) making it possible to calculate the corrected intensity of restitution of a point of the image (103).  13. The method of claim 4; said method being such that said geometric dominant color defects are at least partly characterized by the parameters of a parameterizable rendering transformation model (97) representing the technical characteristics of the means of <Desc / Clms Page number 46>  restitution of the images (19) at the moment of the restitution of the images; said parameters of said parameterizable rendering transformation model (97) for calculating the corrected restitution color of a point of the image (103).  14. The method of claim 5; said method being such that said dive of the view is at least partly characterized by the parameters of a parameterizable rendering transformation model (97) representing the technical characteristics of the image rendering means (19) at the moment of rendering the images. images; said parameters of said parameterizable rendering transformation model (97) for calculating the corrected form of rendering a point of the image (103).  System for providing, in a standard format, to image processing software (4) formatted information (15) related to the characteristics of the image capturing apparatus (1) and / or the means for rendering the image image (19); said image capturing devices (1) being used by users to capture images of scenes (3), including animated scenes (3); said image rendering means (19) being used by users to render captured images (103) by image capture apparatus (1);

 said formatted information (15) comprising: data relating to the intrinsic technical characteristics of the image capture apparatus (1), including distortion characteristics, and / or data relating to the technical characteristics of the capture device; image (1) at the time of image capture, in particular the exposure time, and / or data relating to the intrinsic technical characteristics of the image rendering means (19), in particular the distortion characteristics, and / or <Desc / Clms Page number 47>  data relating to the technical characteristics of the image restitution means (19) at the time of restitution of the images (103), in particular the resolution; said format comprising fields (90) filled with said formatted information (15).  16. System according to claim 15; said formatted data further comprising data relating to the preferences of said user, including the zoom factor.  17. System according to any one of claims 15 or 16 said fields (90) being composed: - Geometric distortion defects.  18. System according to any one of claims 15 to 17; said fields (90) being composed of: - vignetting geometrical defects, and / or - geometric dominant color defects, and / or - of the focal length, and / or - parameters of the method of quantifying the variation of the intensity, and / or - of the parameters of the quantization method of the exposure time, and / or - the parameters of the digital processing carried out by the image-capture device (1), in particular the digital zoom, the compression , and / or parameters representing the preferences of the user, especially with regard to the degree of blur, the resolution of the image.  19. System according to any one of claims 15 to 18; said fields (90) being composed of: - the sharpness of the view, and / or - the depth of field, and / or - the parameters of the method of quantifying the geometry of the sensor, and / or - the parameters of the method of quantifying the noise of the sensor, and / or <Desc / Clms Page number 48>  - parameters of the signal recovery method of the support medium geometry, and / or - parameters of the signal recovery method of the position of the recovery medium, and / or - parameters of the recovery method the noise signal of the image restitution means.  20. System according to any one of claims 15 to 19; said fields (90) being composed of: - gaps (14).  21. System according to claim 17; said format being such that said geometric distortion defects are at least partly characterized by parameters of a parameterizable transformation model (12) representing the technical characteristics of the image capture apparatus (1) at the time of capture image; said parameters of said parameterizable transformation model (12) for calculating the corrected position of a point of the image (103).  22. System according to claim 18; said format being such that said geometric vignetting defects are at least partly characterized by the parameters of a parameterizable transformation model (12) representing the technical characteristics of the image capture apparatus (1) at the moment of capture image; said parameters of said parameterizable transformation model (12) for calculating the corrected intensity of a point of the image (103).  23. The system of claim 18; said format being such that said geometric dominant color defects are at least partly characterized by the parameters of a parameterizable transformation model (12) representing the technical characteristics of the image capture apparatus (1) at the time of the image capture; <Desc / Clms Page number 49>  said parameters of said parameterizable transformation model (12) for calculating the corrected color of a point of the image (103).  24. The system of claim 19; said format being such that said dive of the view is at least partly characterized by parameters of a parameterizable transformation model (12) representing the technical characteristics of the image capture apparatus (1) at the moment of capture image; said parameters of said parameterizable transformation model (12) for calculating the corrected shape of a point of the image (103).  25. The system of claim 17; said format being such that said geometric distortion defects are at least partly characterized by the parameters of a parameterizable rendering transformation model (97) representing the technical characteristics of the image rendering means (19) at the moment of rendering the images. images; said parameters of said parameterizable rendering transformation model (97) for calculating the corrected restitution position of a point of the image (103).  26. The system of claim 18; said format being such that said geometric vignetting defects are at least partly characterized by the parameters of a parameterizable rendering transformation model (97) representing the technical characteristics of the image rendering means (19) at the time of rendering the images. images; said parameters of said parameterizable rendering transformation model (97) making it possible to calculate the corrected intensity of restitution of a point of the image (103).  27. The system of claim 18; said format being such that said geometric dominant color defects are at least partly characterized by the parameters of a parameterizable rendering transformation model (97) representing the technical characteristics of the means of <Desc / Clms Page number 50>  restitution of the images (19) at the moment of the restitution of the images; said parameters of said parameterizable rendering transformation model (97) for calculating the corrected restitution color of a point of the image (103).  28. System according to claim 19; said format being such that said dive of the view is at least partly characterized by the parameters of a parameterizable rendering transformation model (97) representing the technical characteristics of the image rendering means (19) at the time of rendering the images. images; said parameters of said parameterizable rendering transformation model (97) for calculating the corrected form of rendering a point of the image (103).