Device and method for enhancing color contrast based on human color vision features

Abstract

The invention relates to user-specific color contrast enhancement. The present invention proposes a device and a corresponding method for estimating a classification color matching function (CMF) type of a user and / or adjusting a color imaging workflow of a device. The estimating the classification CMF type of the user includes: causing a display screen to display a binary color test set; calculating a score for each classification CMF type in a predetermined set of classification CMF types according to the received input information; and selecting a classification CMF type corresponding to the maximum or minimum score for the user. Furthermore, the invention proposes a device and a corresponding method for creating a binary color test set.

Description

Technical Field

[0001] The present invention relates to user-specific color contrast enhancement. The present invention proposes a device and a corresponding method for estimating the type of categorical color matching function of a user and / or adjusting the color imaging workflow of the device. In addition, the present invention proposes a device and a corresponding method for creating a binary color test set. Background Art

[0002] Color contrast is one of the main characteristics of an image that affects observer perception and the subjective perceived quality of the image. Observer metamerism describes the fact that different observers may have different color perceptions under the same light. Currently, the color imaging workflow of a device is usually based on a predefined single standard observer's color matching function (CMF), which assumes that all observers have the same color vision characteristics. However, the color vision characteristics and the optimal CMF are different for each person.

[0003] In the past, these differences were considered negligible and therefore not taken into account when optimizing devices. However, modern devices have increasingly wider color gamuts. Therefore, ignoring the individual characteristics of human color vision may result in altered or distorted color contrast and a reduction in perceived image quality.

[0004] To achieve a wide color gamut, a very narrow spectral power density (SPD) function, also known as "display primaries," must be implemented on the display. However, since the individual characteristics of each person's CMF vary, a narrower display SPD function may cause the color contrast to be altered or distorted due to more pronounced metameric failure effects. For example, color contrast may be reduced.

[0005] To reduce metameric failures, the traditional personalized color imaging process involves grouping observers into categorical observer types and using pseudo-metamecrometric images to determine the categorical observer type for the user. However, pseudo-metamecrometric images have strict requirements and must be displayed on a special display screen with a large number of display primary colors (at least 9 to 13 theoretical primary colors). In addition, only a limited number of categorical observer types can be distinguished.

[0006] Other conventional procedures include controlling the image display device so that different observers have the same perception of color and color contrast, or using other special equipment, such as color blindness glasses.

[0007] However, in general, conventional processes require fairly specialized hardware to reduce metameric failures. Summary of the invention

[0008] In view of the above, it is an object of the present invention to provide a device and a corresponding method, so that the classified CMF type of a user can be estimated efficiently, while having low and / or flexible hardware requirements. In addition, it is an object of the present invention to improve the perceived image quality and reduce the impact of color contrast distortion caused by observer metameric failure.

[0009] These and other objects are achieved by the inventive solutions described in the independent claims. Advantageous implementations are further defined in the dependent claims.

[0010] A first aspect of the present invention provides a device for estimating a user's color matching function (CMF) type. The device is used to: cause a display screen to display a set of two or more binary color tests, wherein each binary color test corresponds to a pair of a classified CMF type A and a classified CMF type B in a predetermined set of classified CMF types, and each binary color test includes: a first picture of the classified CMF type A in the pair, wherein the first picture includes a color contrast, the color contrast is greater than a first threshold for user A corresponding to the classified CMF type A, and is less than a second threshold for user B corresponding to the classified CMF type B; a second picture of the classified CMF type B in the pair, wherein the second picture includes a color contrast, the color contrast is greater than the first threshold for user B, and is less than the second threshold for user A, the second threshold is a discrimination threshold, and the first threshold is equal to or greater than the second threshold; the device is also used to: calculate a score for each classified CMF type in the predetermined set of classified CMF types according to input information received in response to causing the display screen to display the binary color test set; and select the classified CMF type corresponding to the maximum or minimum score for the user.

[0011] The device provided in the first aspect can efficiently estimate the user's classified CMF type by displaying a binary color test and analyzing the corresponding user input information. Therefore, advantageously, the device provided in the first aspect has lower hardware requirements and / or display requirements. For example, only one display screen is required to determine the user's classified observer type. For example, the device can be a smart phone and / or can include only three display primaries. The device may not require special equipment and / or other equipment, and the display screen can be the screen of a smart phone or a gadget. Compared with traditional solutions, the requirements for the number of different display primaries of the display screen may be lower. By configuring the color imaging process according to the selected CMF type, the device can also reduce the impact of color contrast distortion caused by observer metamerism failure.

[0012] Furthermore, compared to conventional schemes, more classified CMF types can be distinguished and / or the number of considered classified CMF types can be adjusted more flexibly.

[0013] Furthermore, the device is not limited to one binary color test set, but can also use different binary color test sets to flexibly estimate the user's CMF type.

[0014] The first threshold value may be greater than the second threshold value. Alternatively, the first threshold value and the second threshold value may be equal. For example, the first threshold value and the second threshold value may be the same discrimination threshold value.

[0015] The first threshold value and / or the second threshold value may be measured in units of deltaE or dE according to CIEDE2000.

[0016] The discrimination threshold may be a threshold of color contrast for distinguishing between user-perceivable and user-imperceivable color contrasts. For example, according to the CIEDE2000 index, the discrimination threshold may be equal to 2dE, while according to the CIEDE2000 index, the color contrast of a user classified as CMF type A may be less than 2dE and thus cannot be distinguished.

[0017] The discrimination threshold may be equal to a minimum color contrast between two colors that a user is able to discern and / or differentiate.

[0018] The discrimination threshold may be equal to the minimum color contrast that a user can perceive.

[0019] The first threshold and the second threshold may be based on experimental results, for example, testing and comparing various first thresholds and second thresholds.

[0020] In an implementation manner of the first aspect, for each binary color test, the input information includes one of the following situations: the first picture is selected; the second picture is selected; and neither picture is selected.

[0021] In another implementation of the first aspect, each score is calculated based on a weighted sum, each addend in each weighted sum corresponds to a binary color test, and if a picture corresponding to the classified CMF type of the weighted sum is selected, the addend is equal to the weight; otherwise, the addend is equal to zero.

[0022] In another implementation of the first aspect, the predetermined set of classified CMF types is based on two or more display primary colors of the display screen, and / or in the predetermined set of classified CMF types, each different classified CMF type is paired once with each other classified CMF type to form a classified CMF type pair.

[0023] Alternatively, the predetermined set of classified CMF types may be based on three or more display primary colors of a display screen. For example, since a smartphone display screen typically includes exactly three display primary colors, the predetermined set of classified CMF types may be based on the three display primary colors of the display screen.

[0024] In another implementation of the first aspect, for each picture: the color contrast is maximized or almost maximized for users of the corresponding classified CMF type, and the color contrast is minimized or almost minimized for users of the other classified CMF type in the pair.

[0025] In another implementation of the first aspect, each picture includes a color pair of two colors; for each classified CMF type pair, the binary color test set includes M different binary color tests and 2*M different color pairs, respectively, where M is a positive integer greater than 1.

[0026] In another implementation of the first aspect, the device is configured to cause the display screen to display each binary color test in the set twice or more.

[0027] The binary color test set may include two or more of each different binary color test.

[0028] In another implementation of the first aspect, each score is calculated based on a weighted sum, and the device is used to determine the weight of each weighted sum based on one or more of the following weight subtypes: a priori weights; individual a posteriori weights; general a posteriori weights; statistical a posteriori weights.

[0029] In another implementation of the first aspect, the device is further used to perform one or more of the following operations: determining an individual a posteriori weight based on the reliability of the user's selection of each binary color test when the display screen is caused to display each binary color test twice or more; and / or determining a general a posteriori weight based on the total reliability of the user's selection of all binary color tests when the display screen is caused to display each binary color test twice or more.

[0030] The general posterior weight may be a parameter for estimating the reliability of the user as a respondent. Assuming that in the following example, each binary color test is shown to the user twice, and the general posterior weight is normalized, if the general posterior weight is closer to 1, the user's first answer and second answer to the same test tend to be consistent. Therefore, it can be considered that the user is reliable and the user's answer can provide reliable information. If the general posterior weight is less than 0.5, etc., it indicates that the user's answer may be unreliable and the color imaging workflow should be based on a standard observer.

[0031] In another implementation of the first aspect, the prior weight is determined for the corresponding picture by comparing the calculated color contrast of each picture with the calculated color contrast corresponding to the corresponding same classified CMF type pair and classified CMF type and minimizing the objective function; and / or the statistical posterior weight is determined based on statistical information of the reliability of multiple users' selections of each binary color test when the display screen displays each binary color test twice or more.

[0032] In another implementation of the first aspect, the weight of each weighted sum is equal to one of the following weights: the individual posterior weight; the individual posterior weight multiplied by the T norm of the prior weight and the general posterior weight; the individual posterior weight multiplied by the statistical posterior weight and then multiplied by the T norm of the prior weight and the general posterior weight.

[0033] In another implementation of the first aspect, the device is further used to: cause the display screen to display pictures based on the selected classification CMF type and pictures based on the standard observer CMF type; and obtain input information about which CMF type is selected.

[0034] Additional binary color tests are shown on the display to verify the categorical CMF type estimated for the user. If verification is unsuccessful, the standard observer's CMF can be selected instead of the estimated categorical CMF type, or the user's categorical CMF type can be estimated again.

[0035] In another implementation of the first aspect, the device is further configured to configure a color imaging process according to the selected CMF type.

[0036] A second aspect of the present invention provides a device for creating a binary color test set. The device is used to: determine a classification color matching function (color matching function) based on two or more display primary colors of a display screen The invention relates to a method for preparing a color matching function (CMF) type set; creating one or more classified CMF type pairs, wherein each classified CMF type pair includes a classified CMF type A and a classified CMF type B, and in the classified CMF type set, each classified CMF type is paired once with each other classified CMF type; determining a first color pair and a second color pair for each classified CMF type pair, wherein the first color pair has a color contrast, the color contrast is greater than a first threshold for a user A corresponding to the classified CMF type A, and is less than a second threshold for a user B corresponding to the classified CMF type B, and the second color pair has a color contrast, the color contrast is greater than the first threshold for the user B, and is less than the second threshold for the user A, the second threshold is a discrimination threshold, and the first threshold is equal to or greater than the second threshold; creating two pictures including a first picture and a second picture for each classified CMF type pair, wherein the first picture corresponds to the classified CMF type A and includes a pattern of the first color pair, and the second picture corresponds to the classified CMF type B and includes a pattern of the second color pair; creating a first binary color test set, wherein each binary color test corresponds to the classified CMF type pair and includes the corresponding two created pictures.

[0037] The binary color test set may include or consist of the first binary color test set.

[0038] The classified CMF type set may be a predetermined CMF type set provided in the first aspect.

[0039] In the set of categorical CMF types, some categorical CMF types may not be paired with every other categorical CMF type to create one or more categorical CMF type pairs. For example, the number of binary color tests in the set of binary color tests may be reduced by reducing the number of categorical CMF type pairs. A smaller set of binary color tests may require less user input information, but may result in a less accurate estimate of the user's categorical CMF type.

[0040] In an implementation of the second aspect, for each picture: the color contrast is maximized or almost maximized for users of the corresponding classified CMF type, the objective function is minimized or almost minimized based on the color contrast of users of two classified CMF types in the pair, and the color contrast is minimized or almost minimized for users of the other classified CMF type in the pair.

[0041] In another implementation of the second aspect, the almost minimizing of the objective function means that: the color contrast of the two classified CMF types of users in the pair produces at least the Nth minimum objective function output, where N is a positive integer between 2 and 10; or the color contrast of the two classified CMF types of users in the pair produces an objective function output less than a predetermined threshold.

[0042] Alternatively, N may be a positive integer greater than 1.

[0043] Furthermore, nearly minimizing the objective function may refer to the color contrast of the two classified CMF types of users producing a local minimum of the objective function output and / or the color contrast producing the N'''' smallest local minimum of the objective function output, where N''' is a positive integer greater than 1.

[0044] The color contrast may be related to the users of the two classified CMF types in the pair. For example, the color contrast may refer to: a first color contrast of the users of classified CMF type A and a second color contrast of the users of classified CMF type B.

[0045] In another implementation of the second aspect, the device is also used to create one or more other binary color tests for each classified CMF type pair; each image of each other binary color test includes at least one different color compared to each image corresponding to the corresponding same classified CMF type pair; the other binary color tests and the first binary color test set constitute a second binary color test set.

[0046] The binary color test set may include or consist of a second binary color test set.

[0047] In another implementation of the second aspect, the device is further configured to create one or more copies of each binary color test in the first binary color test set or the second binary color test set.

[0048] The binary color test set may include or consist of all binary color tests in the first binary color test set and a copy of each binary color test in the first binary color test set. Alternatively, the binary color test set may include or consist of all binary color tests in the second binary color test set and a copy of each binary color test in the second binary color test set.

[0049] In another implementation of the second aspect, the device is also used to: create a priori weights for each picture by: calculating a first color contrast of the corresponding color pairs; calculating a second color contrast of the color pairs corresponding to the corresponding same classified CMF type pairs and classified CMF types, and minimizing the objective function; and comparing the first contrast and the second color contrast; and / or when the set includes each binary color test twice or more, creating statistical posterior weights based on statistical information of the reliability of multiple users' selections of each binary color test.

[0050] A third aspect of the present invention provides a method of operating a device for estimating a user's categorical color matching function (CMF) type. The method comprises: causing a display screen to display a set of two or more binary color tests, wherein each binary color test corresponds to a pair of categorical CMF type A and categorical CMF type B in a predetermined categorical CMF type set, each binary color test comprising: a first picture of the categorical CMF type A in the pair, wherein the first picture comprises a color contrast, the color contrast being greater than a first threshold for user A corresponding to the categorical CMF type A and less than a second threshold for user B corresponding to the categorical CMF type B; a second picture of the categorical CMF type B in the pair, wherein the second picture comprises a color contrast, the color contrast being greater than the first threshold for user B and less than the second threshold for user A, the second threshold being a discrimination threshold, the first threshold being equal to or greater than the second threshold; the method further comprises: calculating a score for each categorical CMF type in the predetermined categorical CMF type set based on input information received in response to causing the display screen to display the binary color test set; and selecting the categorical CMF type corresponding to the maximum or minimum score for the user.

[0051] The implementation of the method provided in the third aspect may correspond to the implementation of the device provided in the first aspect. The method provided in the third aspect and its implementation achieves the same advantages and effects as the device provided in the first aspect and its corresponding implementation.

[0052] A fourth aspect of the present invention provides a method of operating an apparatus for creating a binary color test set. The method comprises: determining a categorical color matching function (color matching function) based on two or more display primary colors of a display screen; function, CMF) type set; create one or more classified CMF type pairs, wherein each classified CMF type pair includes classified CMF type A and classified CMF type B, and in the classified CMF type set, each classified CMF type is paired once with each other classified CMF type; determine a first color pair and a second color pair for each classified CMF type pair, wherein the first color pair has a color contrast, the color contrast is greater than a first threshold for user A corresponding to the classified CMF type A, and is less than a second threshold for user B corresponding to the classified CMF type B, and the second color pair has a color contrast, the color contrast is greater than the first threshold for user B, and is less than the second threshold for user A, the second threshold is a discrimination threshold, and the first threshold is equal to or greater than the second threshold; create two pictures including a first picture and a second picture for each classified CMF type pair, wherein the first picture corresponds to the classified CMF type A and includes a pattern of the first color pair, and the second picture corresponds to the classified CMF type B and includes a pattern of the second color pair; create a first binary color test set, wherein each binary color test corresponds to the classified CMF type pair and includes the corresponding two created pictures.

[0053] The implementation of the method provided in the fourth aspect may correspond to the implementation of the device provided in the second aspect. The method provided in the fourth aspect and its implementation achieves the same advantages and effects as the device provided in the second aspect and its corresponding implementation.

[0054] A fifth aspect of the present invention provides a computer program product including program code. The computer program product is used to control the device provided by the first aspect or the second aspect or any one of their implementations, or when the program code is executed on a computer, the computer program product is used to execute the method provided by the third aspect or the fourth aspect or any one of their implementations.

[0055] Each observer may have a set of CMFs that determine personalized color perception. Multiple CMF sets of different observers can be divided into groups, namely categorical CMF types, where each group can be described using a CMF set. Each categorical observer type can represent a group or a cluster of individual observers with similar CMF sets. Each observer may correspond to only one categorical observer type, which is determined by the individual CMF set of each observer. The categorical CMF type of a user can be determined by the similarity of the user's individual CMF set to a generalized categorical CMF set. For each type of observer, a specific signal processing flow can be adopted.

[0056] In the present invention, the phrases "classified observer type" and "classified CMF type" may be used interchangeably.

[0057] Furthermore, in the present invention, the terms "user" and "observer" may be used interchangeably.

[0058] Furthermore, in the present invention, the terms "picture" and "image" may be used interchangeably.

[0059] If two binary color tests include exactly the same pictures, then the two may be considered duplicate, identical, and / or indifferent. Alternatively, if two binary color tests include pictures with exactly the same color pairs but different patterns, then the two may be considered duplicate, identical, and / or indifferent. Alternatively, if two binary color tests include pictures with only slightly different color pairs, then the two may be considered duplicate, identical, and / or indifferent.

[0060] In the present invention, the term "reliable" may refer to being consistent and / or conforming to the basic facts or final results. For example, if the value of a sub-weight is consistent with the user's classified CMF type and / or the user's final estimated classified CMF type, then this sub-weight may be reliable.

[0061] It should be noted that all devices, elements, units and components described in the present invention can be implemented in software or hardware elements or any type of combination thereof. All steps performed by various entities described in the present invention and the functions described to be performed by various entities are intended to indicate that the corresponding entities are suitable for or used to perform the corresponding steps and functions. Although in the description of the following specific embodiments, the specific functions or steps performed by the external entity are not reflected in the description of the specific detailed elements of the entity performing the specific steps or functions, it should be clear to the technician that these methods and functions can be implemented by corresponding software or hardware elements or any combination thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In conjunction with the accompanying drawings, the following description of the embodiments will illustrate the above-mentioned various aspects and their implementation methods.

[0063] Figure 1 A device for estimating a user's classified CMF type provided by an embodiment of the present invention is shown.

[0064] Figure 2 A device for creating a binary color test combination provided by one embodiment of the present invention is shown.

[0065] Figure 3 Two sets of CMFs defined based on two popular standard observers are shown.

[0066] Figure 4 A process for determining color pairs classified by an observer provided by one embodiment of the present invention is shown.

[0067] Figure 5 A summary of the calculation steps provided by one embodiment of the present invention to maximize color contrast for a type A classified observer and to minimize color contrast for a type B classified observer is shown.

[0068] Figure 6 The following shows the steps involved in estimating the classified observer type of a user provided by one embodiment of the present invention.

[0069] Figure 7 A method for personalizing color imaging and / or adjusting the color imaging workflow of a device provided by an embodiment of the present invention is shown.

[0070] Figure 8 A method provided by an embodiment of the present invention is shown.

[0071] Fig. 9 A method provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0072] Figure 1 A device 100 is shown for estimating a classified CMF type 101 of a user 300. The device 100 is for causing a display screen 102 to display a set 103 of two or more binary color tests 104. The device 100 may include the display screen 102 or may be connected to the display screen 102. The device 100 may be a smartphone including the display screen 102.

[0073] Each binary color test 104 comprises a first picture 107a of a classified CMF type A 101a and a second picture 107b of a classified CMF type B 101b. The classified CMF type A 101a and the classified CMF type B 101b are comprised in a pair 105 of classified CMF types corresponding to the binary color test 104. Furthermore, the classified CMF type A 101a and the classified CMF type B 101b are comprised in a predetermined set 106 of classified CMF types.

[0074] Figure 1 It is also shown that the device 100 can receive input information 109 from the user 300 in response to causing the display screen 102 to display the set 103 of binary color tests 104. The device 100 is configured to calculate a score 108 for each categorical CMF type 101 in the predetermined set 106 of categorical CMF types based on the received input information 109. In addition, the device is configured to select the categorical CMF type 101 corresponding to the maximum or minimum score 108 for the user 300.

[0075] The first picture 107a includes a color contrast that is greater than a first threshold for user A corresponding to the classified CMF type A 101a, and less than a second threshold for user B corresponding to the classified CMF type B 101b. The second picture 107b includes a color contrast that is greater than the first threshold for user B, and less than the second threshold for user A. The second threshold is a discrimination threshold, and the first threshold is equal to or greater than the second threshold.

[0076] Figure 2 A device 200 is shown for creating a set 103 of binary color tests 104. The device 200 may be a computing device. The device 200 may be integrated with the device 100, but the device 100 and the device 200 are typically separate devices.

[0077] The device 200 is used to determine a set 106 of classified CMF types 101 according to two or more display primaries of a display screen 102, and to create one or more classified CMF type pairs 105. Each classified CMF type pair 105 includes a classified CMF type A 101a and a classified CMF type B 101b, and in the set 106 of classified CMF types, each classified CMF type 101 is paired once with each other classified CMF type 101.

[0078] Furthermore, the device 200 is configured to determine a first color pair 201a and a second color pair 201b for each classified CMF type pair 105, wherein the first color pair 201a has a color contrast ratio that is greater than a first threshold for a user A corresponding to the classified CMF type A 101a and is less than a second threshold for a user B corresponding to the classified CMF type B 101b, and the second color pair 201b has a color contrast ratio that is greater than the first threshold for the user B and is less than the second threshold for the user A. The second threshold is a discrimination threshold, and the first threshold is equal to or greater than the second threshold.

[0079] Furthermore, the device 200 is used to create two pictures including a first picture 107a and a second picture 107b for each classified CMF type pair 105, wherein the first picture 107a corresponds to the classified CMF type A 101a and includes a pattern of the first color pair 201a, and the second picture 107b corresponds to the classified CMF type B 101b and includes a pattern of the second color pair 201b. The first picture 107a and the second picture 107b are included in the binary color test 104.

[0080] Furthermore, the device 200 is used to create a first set 103a of binary color tests, wherein the first set 103a comprises a binary color test for each classified CMF type pair 105, the binary color test corresponding to the classified CMF type pair 105 and comprising the corresponding two created pictures 107a, 107b.

[0081] The binary color test set 103 may include or consist of a first binary color test set 103a. The binary color test set 103 may include or consist of a second binary color test set 103b. The second binary color test set 103b may include the first binary color test set 103a.

[0082] The device 200 may be configured to provide the created first binary color test set 103a to the device 100. Furthermore, the device 200 may be configured to provide the created second binary color test set 103b and / or the created binary color test set 103 to the device 100.

[0083] The CMF set of a specific user 300 of the device 100 may be different from the CMF set of a standard observer. Therefore, optimizing the image processing flow of the device 100 for the standard observer may result in a reduction in color contrast perceived by the user 300. By adjusting the color imaging workflow according to the classified CMF type 101 instead of the CMF set of the standard observer, the device 100 can improve and personalize the color contrast for each specific user 300. The device 100 can estimate the classified CMF type 101 and can provide a corresponding image processing flow.

[0084] The device 100 can estimate the classification CMF type 101 for each user 300 based on the binary color test set 103 generated by the device 200. The device 100 can display each binary color test 104 to the user 300 on the display screen 102, for example, on the display screen of a smartphone. In addition, the device 100 can automatically estimate the classification CMF type 101 of the user 300 using the classification rules for processing the user's response. Some advantages provided by the present invention (especially provided by the device 100 and the device 200) are described in detail below.

[0085] In modern colorimetric theory, a test color stimulus can be matched using an additive mixture of three independent primary color stimuli, where no independent primary color stimulus can be matched using an additive mixture of the other two. The set of three CIE standard CMFs can be used to calculate tristimulus values ​​XYZ according to the following equations:

[0086]

[0087] Where P(λ) is the spectral power distribution of the emission source, is the CIE standard color matching function (CMF), N' is the normalization factor, and I(λ) is the reference white light source.

[0088] Typical color imaging workflows in current conventional equipment are based on a single standard observer and assume that all observers have the same set of CMFs. Figure 3 Two sets of CMFs are shown based on the definitions of two popular standard observers: The CIE standard colorimetric observer has a ten-degree field of view The CIE standard colorimetric observer.

[0089] Classification CMF Type 101 can be categorized based on the metamerism effect. Different Classification 101 observers will perceive color in different ways based on the metamerism phenomenon.

[0090] Categorical observers may classify by finding a color pair that produces a large visual difference for only one categorical observer type 101 and a small visual difference for other categorical observer types 101 .

[0091] Figure 4 A process 400 is shown for determining a color pair 201a, 201b for observer classification based on metamerism effects, wherein a plurality of color pairs including a color α and a color β are tested to determine the color pair 201a, 201b. The color pair 201a, 201b optimizes observer classification within a classified observer type pair 105 including a classified observer type A 101a and a classified observer type B 101b. The process 400 may be performed by the device 200. The input values ​​received in the first step 401 of each loop of the process are the RGB coordinates of the color α and the color β, two CMF sets (CMFs) of two classified CMF types 101 (type A and type B), and two CMFs of the two classified CMF types 101 (type A and type B). A and CMF B ) and the display primary colors (SPD) of the display screen 102.

[0092] The CIEDE2000 metric can be used for visual difference estimation. The coordinates of the color can be defined in the sRGB space. For example, the coordinates of the color α can be (R α , G α , B α ), the coordinates of color β can be (R β , G β , B β ). According to the SPD function (i.e., primary color) of the display screen 102 SPD R (λ), SPD G (λ), SPD B (λ), the spectra of these colors when displayed using the display screen 102 can be determined as follows:

[0093] Sp_color α (λ)=R α ·SPD R (λ)+G α ·SPD G (λ)+R α ·SPD R (λ)

[0094] Sp_color β (λ)=R β ·SPD R (λ)+G β ·SPD G (λ)+B β ·SPD B (λ)

[0095] The calculation of perceived color difference according to the CIEDE2000 formula can be based on the tristimulus values ​​in the CIE 1931 XYZ color space in the color spectrum representation. According to the observer's CMF, the tristimulus values ​​of any spectral color can be obtained. According to the set of color matching functions of the classified observer type k 101, the XYZ coordinates of these colors can be obtained. Based on these coordinates, the CIEDE2000 color difference between the two colors can be calculated.

[0096] The categorical CMF type set 106 may include 10 categorical CMF types 101. However, more or fewer categorical CMF types 101 may be used. On the one hand, the number of binary color tests depends on the number of categorical CMF types 101. Therefore, it is better to reduce the number of categorical CMF types 101. On the other hand, increasing the number of categorical CMF types 101 may achieve a more accurate classification.

[0097] The CIEDE2000 index can be calculated based on two colors α and β that show the visual color contrast for categorical observer type k 101:

[0098]

[0099] Among them, x_cat k (λ), y_cat k (λ), z_cat k (λ) is the CMF of the categorical observer of type k 101.

[0100] Figure 4 A second step 402 is shown, which includes calculating the distance between the color α and the color β of the classified observer type A 101a and the classified observer type B 101b according to CIEDE2000. and

[0101] The first step 401 and the second step 402 may be repeated one or more times to calculate the color distances between a plurality of color pairs including the color α and the color β, respectively.

[0102] Figure 4 The third step 403 in comprises determining D A is the maximum value, D B is the minimum value of the two colors α and β. This means that the optimization task is performed on all possible pairs of colors α and β.

[0103] To find two colors α and β, such that the classified observer type A 101a To achieve the maximum value, the function to solve this minimization task can be defined as follows:

[0104]

[0105] In addition, the color α and β pair 201a, 201b (the initial coordinates of color α are (R α , G α , B α ), the initial coordinates of color β are (R β , G β , B β )) should minimize or nearly minimize the CIEDE2000 index of the XYZ coordinates obtained by the CMF of other categorical observer types 101 (eg, categorical observer type B 101b).

[0106] and

[0107] Where, ΔE thr is the threshold value.

[0108] Based on these conditions, an objective function may be created that maximizes visual color contrast for one categorical observer type 101 (eg, type A 101a) and minimizes visual color contrast for other categorical observer types 101 (eg, type B 101b).

[0109] The optimization task may include finding the colors α and β that minimize or nearly minimize the objective function. For example, for an observer pair 105 of categorical observer type A 101a (cat_A) and categorical observer type B 101b (cat_B), the objective function may be defined according to the following equation, where cat_A is the target categorical observer:

[0110]

[0111] F(x)=(x-thr max )2

[0112] Threshold thr max It can be determined experimentally. According to Huawei Mate 20Pro SPD for three thresholds thr max (i.e., 1, 2, and 5) Experiments to solve the above optimization problem show that the color contrast between the two colors for the target observer (Observer A) and the non-target observer (Observer B) may not change significantly. Therefore, using very large thresholds may not be recommended. Based on further analysis of the resulting test images, a feasible threshold value, thr max = 2. Other thresholds may also be used.

[0113] Determining the color pair 201a, 201b may be achieved by minimizing or nearly minimizing the following objective function:

[0114]

[0115] By minimizing or nearly minimizing the objective function, two colors 201a can be determined that have a large color contrast for an observer who classified CMF type A 101a and a small color contrast for an observer who classified CMF type B 101b, ie, are similar in color.

[0116] Nearly minimizing the objective function may refer to determining variable arguments of the objective function, e.g., color pairs and classified CMF type pairs 105, or color contrast of user 300 of classified CMF type A 101a and color contrast of user 300 of classified CMF type B 101b, so as to produce one or more of: at least the Nth minimum objective function output; an objective function output that is less than a predetermined threshold; a local minimum of the objective function output; the N''th smallest local minimum of the objective function output.

[0117] N may be a positive integer greater than 1, N'' may be a positive integer greater than 1, and the predetermined threshold may be determined by testing and / or comparing multiple threshold iterations and / or based on experiments.

[0118] The variable parameters (e.g., color pairs and classified CMF type pairs 105, or color contrast) that minimize or nearly minimize the objective function can be iteratively determined and / or experimentally determined by testing and / or comparing multiple variable parameters. For example, multiple objective function outputs for multiple color contrasts can be calculated and compared to each other and / or to one or more of the above requirements.

[0119] Figure 4 The fourth step 404 in the embodiment includes drawing a special pattern (i.e., a picture) using the color pair 201a obtained in the third step 403, which can distinguish between observer type A 101a and observer type B 101b. The process 400 can be repeated to create a pattern using other color pairs 201b, which makes the distance D B Maximize and make the distance D A Therefore, two pictures 107a, 107b may be created for each CMF type pair 105. Furthermore, the creation of two pictures 107a, 107b as described above may be repeated for other classified observer type pairs 105, wherein each two pictures 107a, 107b may form a binary color test 104.

[0120] The minimization task can be solved for all possible pairs of classified observer types 105. The number of pairs of classified observer types N" can be determined according to the following combination equation:

[0121]

[0122] where K is the number of categorical observer types 101. Thus, the total number of different test pictures in the binary color test set 103, 103a may be 2N", etc., with one binary color test 104 for each categorical CMF type pair 105, and each binary color test 104 corresponding to two pictures 107a, 107b.

[0123] The binary color test set 103 may include a plurality of different binary color tests 104 for each categorical observer type pair 105. Furthermore, the binary color test set 103 may also include copies and / or slightly modified copies of each different binary color test 104.

[0124] The binary color test 104 can be created based on the determined color pairs 201a, 201b. Each binary color test 104 can include two pictures 107a, 107b in the form of circles, wherein each circle can include a symbol or pattern on a background. On the first circle 107a, the color difference between the symbol and the background may be obvious to an observer of classification type A 101a, but not obvious to an observer of classification type B 101b. On the second picture 107b, the color difference between the symbol and the background may be obvious to an observer of classification type B 101b, but not obvious to an observer of classification type A 101b. A variety of different shapes and patterns can be used.

[0125] Figure 5 A summary of the calculation steps described above is shown to find a color pair 201a that maximizes the color contrast of a classification observer of type A 101a and minimizes the color contrast of a classification observer of type B 101b. The calculation steps may be performed by the device 200.

[0126] The observer can be classified according to the user input information 109 associated with the binary color test set 103. The observer classification can be performed by the device 100. The classification rules can be based on pairwise classification. According to the classification results of all picture pairs 107a, 107b (i.e., binary color tests 104), it can be determined whether the user 300 belongs to one of the categories, i.e., the classified CMF type 101.

[0127] The experimental results show that it is best to implement the classification rule for determining the classification observer type 101 according to the "number of votes" of each classification observer type 101. The "number of votes" refers to the number of times during the test that the user 300 selects the image corresponding to the specific classification observer type 101, which has a more significant color contrast.

[0128] Each binary color test 104 may be displayed multiple times to the user 300. Alternatively, for each binary color test 104, multiple binary color tests 104 may be created that include the same color pair 201a, 201b but different patterns.

[0129] For each binary color test 104, the user 300 may select the first image 107a, the second image 107b, or neither image, for example, skipping the binary color test 104. Based on one or more selections of the user 300, matching parameters may be introduced. In a preferred example, each different binary color test 104 may be displayed twice to the user 300. Therefore, the user input information 109 of each different binary color test 104 may include two answers: answer1 and answer2, that is, each same binary color test 104 corresponds to one answer.

[0130] Matching parameters It can be defined as follows:

[0131]

[0132] Assuming that the user 300 is shown the same binary color test 104 twice and each time selects the same picture 107a corresponding to categorical observer type A 101a instead of the picture 107b corresponding to categorical observer type B 101b, the matching parameters of the two binary color tests 104 are Both binary color tests 104 may be considered when calculating score 108 and estimating the classified CMF type 101 of user 300. Therefore, the matching parameters of the two binary color tests 104 may add up to 1, indicating a perfect reliability of user 300 for binary color test i 104.

[0133] In addition, the score 108 corresponding to the classified observer type A 101a increases, while the score 108 corresponding to the classified observer type B 101b remains unchanged. Assuming that the user 300 selects the picture 107b corresponding to the classified observer type B 101b, the score 108 corresponding to the classified observer type B 101b increases, while the score 108 corresponding to the classified observer type A 101a remains unchanged.

[0134] Generally speaking, the matching parameters of each binary color test 104 are the same as those of the binary color test 104i. It can be defined as follows:

[0135]

[0136] Wherein N1 is the number of times the user 300 selects the picture 107a corresponding to the classified observer type A 101a when the binary color test i 104 or one of the binary color tests 104 identical to the binary color test i 104 is displayed, N2 is the number of times the user 300 selects the picture 107b corresponding to the classified observer type B 101b when the binary color test i 104 or one of the binary color tests 104 identical to the binary color test i 104 is displayed, and N3 is the number of times the binary color test i 104 or a binary color test 104 identical to the binary color test i 104 is displayed on the display screen 102 and / or displayed to the user 300.

[0137] The matching parameter may be normalized based on the number of copies of each different binary color test 104 in the binary color test set 103 .

[0138] The matching parameters may be normalized differently than in the above example.

[0139] The matching parameters for each identical binary color test 104 may be identical.

[0140] For example, the matching parameters may be defined in other ways, where reliable choices are scored 108 higher than unreliable choices.

[0141] For example, the matching parameters may be defined in other ways, where reliable choices are scored lower 108 compared to unreliable choices.

[0142] Matching parameters It can also be called individual posterior weight

[0143] The score 108 of the category observer type A 101a (Cat_type[A]) can be calculated based on the “number of votes” as follows:

[0144]

[0145] Here, R_weight_i may be based on one or more other sub-weights corresponding to the binary color test i 104 and the same binary color test 104 as the binary color test i 104 .

[0146] Alternatively, the score 108 for Cat_type[A] may be defined as follows:

[0147]

[0148] Wherein, all sub-weight types are combined into a total weight corresponding to the binary color test i 104 and the binary color test 104 that is the same as the binary color test i 104

[0149] The total weight may depend on the user's 300 answers to each binary color test 104. For each binary color test 104, there may be two total weights: and At least one of the total weights of each binary color test 104 may represent a minimum reliability, for example, may be equal to 0. Therefore, one of the total weights of each binary color test 104 may be ignored and / or not added to the score 108, thereby not increasing the score 108.

[0150] The sub-weights may depend on the user's 300 answer to each binary color test 104. For each binary color test 104, each sub-weight type may have two sub-weights, e.g. and and / or and

[0151] The weight of each binary color test 104 may be based on a priori sub-weights and a posteriori sub-weights.

[0152] A priori weights may be based on estimates based on the CIEDE2000 indicators.

[0153] Other binary color tests 104 may be used and / or created, for example, added to the first binary color test set 103a. The other binary color tests 104 may include pictures based on other color pairs that correspond to local minima of the objective function and / or nearly minimize the objective function.

[0154] For each image, a color pair and its CIEDE2000 metric are available. To determine the prior weights, the CIEDE2000 indicator corresponds to the corresponding classified CMF type 101 of the image, and according to the corresponding classified CMF type 101 of the image and the classified CMF type pair 105 corresponds to the global minimum value of the objective function.

[0155] In addition, other color pairs and their CIEDE2000 indices may be used to determine the prior weights. Other color pairs may correspond to local minima of the objective function and / or may nearly minimize the objective function according to the corresponding classified CMF type 101 and classified CMF type pair 105 of the picture. In one example, other color pairs corresponding to the second and third local minima of the objective function may be calculated according to the CIEDE2000 indices. Thus, the total number of different binary color tests 104 of the binary color test set 103, 103a can be increased for statistical verification. The other binary color tests and the first binary color test set 103a can constitute the second binary color test set 103b.

[0156] Compared to the pair corresponding to the global minimum of the objective function, the second and third color pairs may have lower reliability and / or may contribute less to estimating the classified CMF type 101 of the user 300. This may be taken into account by introducing corresponding prior weights. The prior weights may be calculated as follows:

[0157]

[0158] The posterior weights may include one or more of the following sub-weights: individual posterior weights, general posterior weights, and statistical posterior weights.

[0159] The general a posteriori weights can be based on the reliability of the user input information 109. Information about the reliability of the binary color test set 103 for a particular user 300 can be obtained based on the experimental results of the binary color test set 103 performed by the particular user 300. In a preferred example, each binary color test 104 can be displayed twice. Based on the answers of the user 300, the matching parameters can be used.

[0160]

[0161] The general posterior weights can be determined as follows:

[0162]

[0163] Wherein, N' is the total number of different binary color tests 104 in the binary color test set 103. The general posterior weight may be the average of the individual posterior weights. Therefore, the general posterior weight may represent the general reliability of a particular user 300 and the corresponding user input information 109, which is based on all binary color tests 104 in the binary color test set 103. For example, if the user 300 randomly selects pictures in each binary color test 104, the overall reliability of the user may be considered low according to the general posterior weight. In this case, the user input information 109 may not provide accurate information about the classified CMF type 101 of the user 300, and may therefore be ignored. For a particular user 300, the general posterior weights of all binary color tests 104 in the binary color test set 103 may be the same.

[0164] The statistical a posteriori weights may be based on the reliability of the user input information 109 of the plurality of users 300 for each binary color test 104. Experiments may show that some binary color tests 104 are statistically less reliable than other binary color tests 104. A less reliable binary color test 104 may be replaced with a more reliable binary color test 104. Furthermore, a less reliable binary color test 104 may have a smaller statistical a posteriori weight. Therefore, a less reliable binary color test 104 may have a reduced impact on the estimated classified CMF type 101.

[0165] The statistical posterior weights can be determined as follows:

[0166]

[0167] Here, N′ is the total number of different binary color tests 104 in the binary color test set 103 , and M′ is the total number of users 300 considered for the statistical evaluation of the binary color test set 103 .

[0168] One or more sub-weights may be combined into a corresponding total weight. For example, each sub-weight may be multiplied to determine each total weight. Alternatively, one or more sub-weight types may not be used.

[0169] Sub-weights can be combined with one or more other weight sub-types according to the T-norm from fuzzy logic theory:

[0170]

[0171] Sub-weights can be combined with one or more other weight sub-types according to other functions, e.g.

[0172]

[0173] For example, the total weight can be defined as follows:

[0174]

[0175] The individual a posteriori weights and the general a posteriori weights may be determined by device 100, for example, in a smartphone. The a priori weights and the statistical a posteriori weights may be determined by device 200, for example, in an external processing device. The external processing device may have access to more processing power and / or more data than device 100. The weights calculated in device 200 may be provided to device 100. Alternatively, all weights may be determined in device 100.

[0176] Figure 61 shows the steps that may be involved in estimating the categorical observer type 101 of the user 300. The steps may be performed by the device 100. In this example, the binary color test set 103 is based on 5 categorical observer types 101. Each categorical observer type 101 is paired once with each other categorical observer type 101, resulting in 10 categorical observer type pairs 105. The binary color test set 103 is displayed to the user 300, wherein each different binary color test 104 is displayed twice in a random order. Thus, a total of 20 binary color tests 104 are displayed to the user 300. Each binary color test 104 includes two pictures 107a, 107b, thereby providing a selection opportunity for each binary color test 104.

[0177] also, Figure 6 An exemplary binary color test 104 is shown including two circles, each circle including a pattern. It is worth noting that the binary color test 104 is color sensitive. Figure 6 The exemplary pictures in are for reference only. The device 100 can cause the display screen 102 to display a binary color test 104. The user 300 can choose whether to prefer the right circle or the left circle, or whether the binary color test 104 should be skipped. If the user 300 perceives that the color contrast of one picture is greater than the color contrast of the other picture and / or perceives that the pattern of one picture is clearer than the pattern of the other picture, the first picture may be preferred. For example, the pattern can be numbers or letters. If the user 300 cannot determine which picture is preferred, the binary color test 104 can be skipped. In Figure 6 In the illustrated exemplary binary color test 104 , the left image 107 a corresponds to categorical observer type A 101 a and the right image 107 b corresponds to categorical observer type B 101 b .

[0178] Based on the answers of the user 300, the device 100 receives user input information 109 (response). Based on the user input information 109, a weight can be calculated for the binary color test 104. The matching parameter can be calculated based on the two separate answers received from the user 300 regarding the exemplary binary color test 104. For example, if the user 300 prefers the left picture 107a in both answers, the matching parameter can be large and / or equal to 0.5, while the matching parameter can be small and / or equal to 0. In addition, other sub-weights can be calculated. The score 108 of the classified observer type A 101a can be increased by multiplying the matching parameter by the other sub-weights and then adding the result to the score 108. On the other hand, since the matching parameter may be equal to 0, so the score 108 for the classified observer type B 101b may not increase. The above adjustment score 108 may be repeated for each binary color test 104 in the binary color test set 103. Based on the final score 108, the user 300 may be assigned the category with the maximum corresponding score 108 (i.e., weighted sum).

[0179] According to an embodiment of the present invention, a method may include a first step and a second step. For example, the first step may be performed when a smartphone (device 100) is initialized or in other calibration processes, including displaying a binary color test set 103 and obtaining user input information 109; the second step may be applied to the color imaging process of the device 100, wherein the standard observer CMF may be replaced by a determined classification CMF type 101. The method may be performed by the device 100.

[0180] A set of binary color tests 103 may be displayed to a user of the device 100, wherein each binary color test 104 may consist of two pictures 107a, 107b. Furthermore, each picture 107a, 107b may include a symbol on a background, wherein each picture 107a, 107b may be determined according to two classified CMF types 101a, 101b. The number of binary tests 104 may be determined according to the severity of the metameric failure of the display screen 102 on which the binary color test 104 is displayed.

[0181] The user 300 may select the picture 107a, 107b in which the perceived color difference between the background and the symbol is more obvious. For example, if the user 300 does not perceive the color contrast advantage of one of the pictures 107a, 107b relative to the other, the user 300 may decline the selection. The user 300 may visually compare the distinguishability of the stimulus from the background in the two images 107a, 107b of each binary color test 104.

[0182] Based on the automatic analysis of the user's 300 answers on the binary color test set 103, the categorical CMF type 101 of the user 300 can be estimated, wherein the categorical CMF type 101 that is most similar to the individual CMF type 101 of the user 300 can be selected. In addition, the determination of the categorical CMF type 101 of the user 300 can be based on one or more sub-weights. The sub-weights can represent the reliability of the binary color test 104 and the reliability of the user's 300 answer.

[0183] Based on the identified classified CMF type 101, a special test image may be formed and displayed to the user 300 to verify the results of the test.

[0184] Based on the estimated classified CMF type 101 for the user 300 , a new color imaging workflow may be implemented to account for personalized color reproduction, wherein the color workflow may be adjusted from a color workflow corresponding to a standard CMF type observer to a color workflow corresponding to the estimated classified CMF type 101 .

[0185] Figure 7 A method 700 for personalizing color imaging and / or adjusting a color imaging workflow of a device is shown. The method 700 may be performed by a device 100. Imaging input data for visualizing a picture on a display screen 102 of the device 100 may be acquired 701 using a general device-independent format (e.g., sRGB format). The imaging input data may be subjected to a standard correction process 702, including one or more of the following processes: linearization (gamma correction), reference white point (D65) consideration, white balancing, color transformation, and / or other processes, and may be converted 703 to an XYZ format.

[0186] Colors can be rendered on the display screen 102 by the display primaries of the display screen 102. The resulting spectrum emitted by the display screen 102 can be perceived by the user 300, wherein the perception of the user 300 depends on the individual CMF of the user 300. For example, CMF variations between people are caused by the individual distribution of receptors on the retina. In addition, based on the input information 109 of the user 300, the categorical observer type of the user 300 can be estimated.

[0187] For personalized color correction 704, the imaging data may be converted from an XYZ format for a standard observer to an XYZ' format for a specific observer type 705, wherein personal characteristics of the user 300 may be taken into account. Furthermore, the imaging data may be converted to a display color space 706. The final data may again be subjected to a standard correction process and may be rendered 707 and finally displayed on the display screen 102.

[0188] Figure 8 1 shows a method 800 provided by an embodiment of the present invention. The method 800 may be performed by the device 100. The method 800 comprises step 801: causing the display screen to display a set of two or more binary color tests. In addition, the method 800 comprises step 802: calculating a score for each classified CMF type in a predetermined set of classified CMF types according to input information received in response to causing the display screen to display the set of binary color tests. In addition, the method 800 comprises step 803: selecting the classified CMF type corresponding to the maximum or minimum score for the user 300.

[0189] Fig. 9A method 900 provided by an embodiment of the present invention is shown. The method 900 can be executed by the device 200. The method 900 includes step 901: determining a set of classified CMF types according to two or more display primary colors of a display screen. In addition, the method 900 includes step 902: creating one or more classified CMF type pairs, wherein each classified CMF type pair includes classified CMF type A and classified CMF type B. In addition, the method 900 includes step 903: determining a first color pair for each classified CMF type pair, wherein the first color pair has a color contrast ratio, which is greater than a first threshold for user A corresponding to the classified CMF type A, and is less than a second threshold for user B corresponding to the classified CMF type B. In addition, the method 900 includes step 904: determining a second color pair for each classified CMF type pair, wherein the second color pair has a color contrast ratio, which is greater than the first threshold for user B, and is less than the second threshold for user A. Furthermore, the method 900 comprises step 905 of creating two pictures including a first picture and a second picture for each classified CMF type pair, wherein the first picture corresponds to the classified CMF type A and includes a pattern of the first color pair, and the second picture corresponds to the classified CMF type B and includes a pattern of the second color pair. Furthermore, the method 900 comprises step 906 of creating a first binary color test set, wherein each binary color test corresponds to the classified CMF type pair and includes the corresponding two created pictures.

[0190] Experiments show that 88% of users select the classified CMF type determined individually according to an embodiment of the present invention as the preferred CMF type, that is, approximately 88% of users indicate that the color contrast is improved after color transformation according to the individually determined classified CMF type.

[0191] After the color correction process for the corresponding categorical observers, the CIEDE2000 color difference increased by an average of 1.75 times, thus enabling users to distinguish colors that were indistinguishable before the color correction process.

[0192] According to an embodiment of the present invention, the classification CMF type can be determined based on the color pairs with the smallest color difference in CIEDE2000. Therefore, compared with the traditional solution, the requirement on the number of different display primary colors of the display screen can be reduced.

[0193] The present invention has been described in conjunction with various embodiments as examples and implementations. However, from a study of the drawings, the present invention and the independent claims, other variations will be understood and implemented by those skilled in the art when implementing the claimed subject matter. In the claims and in the specification, the word "comprising" does not exclude other elements or steps, and the quantifier "a" does not exclude a plurality. A single element or other unit may fulfil the functions of several entities or items described in the claims. The listing of certain measures in mutually different dependent claims does not imply that a combination of these measures cannot be used in an advantageous implementation.

Claims

1. A device (100) for estimating a categorical color matching function (CMF) type (101) of a user (300), characterized in that The device (100) is used for: causing a display screen (102) to display a set (103) of two or more binary color tests (104), Wherein each binary color test (104) corresponds to a pair (105) of classified CMF type A (101a) and classified CMF type B (101b) from a predetermined set (106) of classified CMF types, each binary color test (104) comprising: a first picture (107a) of the classified CMF type A (101a) of the pair (105), wherein the first picture (107a) comprises a color contrast ratio that is greater than a first threshold for a user A corresponding to the classified CMF type A (101a) and less than a second threshold for a user B corresponding to the classified CMF type B (101b), a second image (107b) of the classified CMF type B (101b) in the pair (105), wherein the second image (107b) comprises a color contrast, the color contrast being greater than the first threshold for the user B and less than the second threshold for the user A, the second threshold being a discrimination threshold, the first threshold being equal to or greater than the second threshold; The device (100) is also used for: calculating a score (108) for each categorical CMF type (101) in the predetermined set (106) of categorical CMF types based on input information (109) received in response to causing the display screen (102) to display the set (103) of binary color tests (104); A classification CMF type (101) corresponding to the maximum or minimum score (108) is selected for the user (300).

2. The device (100) according to claim 1, characterized in that For each binary color test (104), the input information (109) includes one of the following situations: the first picture (107a) is selected; the second picture (107b) is selected; and neither picture is selected.

3. The device (100) according to claim 2, characterized in that Each score (108) is calculated based on a weighted sum, each addend in each weighted sum corresponds to a binary color test (104), if the picture corresponding to the classification CMF type (101) of the weighted sum is selected, then the addend is equal to the weight; otherwise, the addend is equal to zero.

4. The device (100) according to any one of the preceding claims, characterized in that The predetermined set (106) of classified CMF types is based on two or more display primaries of the display screen (102), and / or in the predetermined set (106) of classified CMF types, each different classified CMF type (101) is paired once with each other classified CMF type (101) to form a classified CMF type pair.

5. The device (100) according to any one of the preceding claims, characterized in that For each image: the color contrast is maximized or nearly maximized for users of the corresponding classified CMF type (101), The color contrast is minimized or nearly minimized for users of the other classified CMF type (101) of the pair.

6. The device (100) according to any one of the preceding claims, characterized in that Each picture includes a color pair of two colors; for each classified CMF type pair, the binary color test set (103) includes M different binary color tests (104) and 2*M different color pairs, respectively, where M is a positive integer greater than 1.

7. The device (100) according to any one of the preceding claims, characterized in that The device (100) is used to cause the display screen (102) to display each binary color test (104) in the set (103) twice or more.

8. The device (100) according to any one of the preceding claims, characterized in that Each score (108) is calculated based on a weighted sum, and the apparatus (100) is configured to determine a weight for each weighted sum based on one or more of the following weight subtypes: Prior weights; Individual posterior weights; general posterior weights; Statistical posterior weights.

9. The device (100) according to claims 7 and 8, characterized in that The device (100) is further configured to perform one or more of the following operations: When causing the display screen (102) to display each binary color test (104) two or more times, determining individual posterior weights based on the reliability of the user's (300) selection of each binary color test (104); and / or When the display screen (102) is caused to display each binary color test (104) two or more times, a general a posteriori weight is determined based on the total reliability of the user's (300) selection of all binary color tests (104).

10. The device (100) according to claim 8 or 9, characterized in that The a priori weights are determined for each picture by comparing the calculated color contrast of the picture with the calculated color contrast corresponding to the corresponding same classified CMF type pair (105) and the classified CMF type (101) and minimizing an objective function; and / or The statistical a posteriori weight is determined based on statistical information of the reliability of selections of each binary color test (104) by a plurality of users when the display screen (102) displays each binary color test (104) two or more times.

11. The device (100) according to any one of claims 8 to 10, characterized in that The weight of each weighted sum is equal to one of the following weights: the individual posterior weights; The individual posterior weight is multiplied by the T norm of the prior weight and the general posterior weight; The individual posterior weight is multiplied by the statistical posterior weight and then multiplied by the T norm of the prior weight and the general posterior weight.

12. The device (100) according to any one of the preceding claims, characterized in that The device (100) is also used for: The display screen (102) displays a picture based on the selected classification CMF type (101) and a picture based on the standard observer CMF type; Input information (109) is obtained regarding which CMF type (101) is selected.

13. The device (100) according to any one of the preceding claims, characterized in that The device (100) is also used to configure a color imaging process according to the selected CMF type (101).

14. A device (200) for creating a set (103) of binary color tests (104), characterized in that The device (100) is used for: Determining a set (106) of classified color matching function (CMF) types (101) according to two or more display primary colors of a display screen (102); creating one or more classified CMF type pairs (105), wherein each classified CMF type pair (105) comprises a classified CMF type A (101a) and a classified CMF type B (101b), wherein each classified CMF type (101) is paired once with each other classified CMF type (101) in the classified CMF type set (106); Determine a first color pair (201a) and a second color pair (201b) for each classified CMF type pair (105), wherein the first color pair (201a) has a color contrast ratio, the color contrast ratio is greater than a first threshold for a user A corresponding to the classified CMF type A (101a), and is less than a second threshold for a user B corresponding to the classified CMF type B (101b), the second color pair (201b) has a color contrast ratio, the color contrast ratio is greater than the first threshold for the user B, and is less than the second threshold for the user A, the second threshold is a discrimination threshold, and the first threshold is equal to or greater than the second threshold; creating two pictures including a first picture (107a) and a second picture (107b) for each classified CMF type pair (105), wherein the first picture (107a) corresponds to the classified CMF type A (101a) and includes a pattern of the first color pair (201a), and the second picture (107b) corresponds to the classified CMF type B (101b) and includes a pattern of the second color pair (201b); A first set (103a) of binary color tests (104) is created, wherein each binary color test (104) corresponds to a classified CMF type pair (105) and comprises the corresponding two created pictures.

15. The device (200) according to claim 14, characterized in that For each image: the color contrast is maximized or nearly maximized for users of the corresponding classified CMF type (101), By minimizing or nearly minimizing the objective function based on the color contrast for users of two classified CMF types in the pair, the color contrast is minimized or nearly minimized for users of the other classified CMF type (101) in the pair.

16. The device (200) according to claim 15, characterized in that The objective function is almost minimized as follows: The color contrast ratio of the users of the two classified CMF types in the pair produces at least the Nth minimum objective function output, where N is a positive integer between 2 and 10; or The color contrast of the user of the two classified CMF types in the pair produces an objective function output that is less than a predetermined threshold.

17. The device (200) according to any one of claims 14 to 16, characterized in that The apparatus (200) is further configured to create one or more other binary color tests (104) for each classified CMF type pair; Each picture of each other binary color test (104) includes at least one different color compared to each picture corresponding to the corresponding same-classification CMF type pair; The further binary color tests (104) and the first binary color test set (103a) form a second binary color test set (103b).

18. The device (200) according to any one of claims 14 to 17, characterized in that The apparatus (200) is further configured to create one or more copies of each binary color test (104) in the first binary color test set (103a) or the second binary color test set (103b).

19. The device (200) according to any one of claims 15 to 18, characterized in that The device (200) is also used for: Create a prior weight for each image by doing the following: Calculating a first color contrast ratio of the corresponding color pair; Calculating a second color contrast ratio of a color pair corresponding to the corresponding same classified CMF type pair (105) and the classified CMF type (101), and minimizing the objective function; and comparing the first contrast and the second color contrast; and / or When the set (103) includes each binary color test (104) twice or more, a statistical posterior weight is created based on statistical information of the reliability of the selections of each binary color test (104) by multiple users.

20. A method (800) of operating a device (100) for estimating a user's categorical color matching function (CMF) type (101), characterized in that: The method (800) comprises: causing (801) a display screen (102) to display a set (103) of two or more binary color tests (104), Wherein each binary color test (104) corresponds to a pair (105) of classified CMF type A (101a) and classified CMF type B (101b) from a predetermined set (106) of classified CMF types, each binary color test (104) comprising: a first picture (107a) of the classified CMF type A (101a) of the pair, wherein the first picture (107a) comprises a color contrast that is greater than a first threshold for a user A corresponding to the classified CMF type A (101a) and less than a second threshold for a user B corresponding to the classified CMF type B (101b), a second image (107b) of the classified CMF type B (101b) in the pair, wherein the second image (107b) comprises a color contrast ratio, the color contrast ratio being greater than the first threshold for the user B and less than the second threshold for the user A, the second threshold being a discrimination threshold, the first threshold being equal to or greater than the second threshold; The method (800) further comprises: calculating (802) a score (108) for each categorical CMF type (101) in the predetermined set (106) of categorical CMF types based on input information (109) received in response to causing the display screen (102) to display the binary color test set (103); The classification CMF type (101) corresponding to the maximum or minimum score is selected (803) for the user.

21. A method (900) of operating an apparatus (200) for creating a set (103) of binary color tests (104), characterized in that The method (900) comprises: Determining (901) a set of classified color matching function (CMF) types (101) according to two or more display primary colors of a display screen (102); creating (902) one or more classified CMF type pairs, wherein each classified CMF type pair (105) comprises a classified CMF type A (101a) and a classified CMF type B (101b), wherein each classified CMF type (101) is paired once with each other classified CMF type (101) in the set of classified CMF types; determining (903, 904) a first color pair and a second color pair for each classified CMF type pair, wherein the first color pair has a color contrast ratio, the color contrast ratio being greater than a first threshold for a user A corresponding to the classified CMF type A (101a) and less than a second threshold for a user B corresponding to the classified CMF type B (101b), the second color pair has a color contrast ratio, the color contrast ratio being greater than the first threshold for the user B and less than the second threshold for the user A, the second threshold being a discrimination threshold, and the first threshold being equal to or greater than the second threshold; creating (905) two pictures for each classified CMF type pair, including a first picture (107a) and a second picture (107b), wherein the first picture (107a) corresponds to the classified CMF type A (101a) and includes a pattern of the first color pair, and the second picture (107b) corresponds to the classified CMF type B (101b) and includes a pattern of the second color pair; A first set of binary color tests is created (906), wherein each binary color test (104) corresponds to a classified CMF type pair (105) and includes the corresponding two created pictures.

22. A computer program product comprising program code, characterized in that The computer program product is used to control the device according to any one of claims 1 to 19, or when the program code is executed on a computer, the computer program product is used to perform the method (800, 900) according to claim 20 or 21.

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