Chromaticity attribute evaluation method and apparatus, computer device, storage medium, and product

Abstract

A chromaticity attribute evaluation method and apparatus (200), a computer device, a storage medium, and a product. The chromaticity attribute evaluation method comprises: calculating a first chromatic aberration ΔE1 of a chromatic aberration-free group under an illumination light source, the chromatic aberration-free group being a combination of a standard sample and a chromatic aberration-free product (S100); calculating a second chromatic aberration ΔE2 of a group being evaluated under an illumination light source, the group being evaluated being a combination of a standard sample and a product being evaluated (S200); acquiring a false positive probability of determining a chromatic aberration-free group as "having a chromatic aberration" and a true positive probability of determining a group being evaluated as "having a chromatic aberration" (S300); inputting the false positive probability and the true positive probability into a quantization model of the chromatic aberration resolution threshold to obtain a chromatic aberration resolution threshold of the product being evaluated (S400); inputting the false positive probability and the true positive probability into a quantization model of the chromatic aberration determination bias to obtain an estimated determination bias value of the product being evaluated (S500); and on the basis of the chromatic aberration resolution threshold and the estimated determination bias value, acquiring the chromaticity attribute evaluation result of the product being evaluated (S600).

Description

Method and device for evaluating chroma attribute, computer device, storage medium and product

[0001] Cross-reference to related applications

[0002] The present application claims priority to the Chinese patent application No. 202411315053.2, filed on September 19, 2024, and entitled "Method and device for evaluating chroma attribute, computer device, storage medium and product", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0003] The present disclosure relates to the technical field of color discrimination, and particularly relates to a method and device for evaluating chroma attribute, a computer device, a storage medium and a product. BACKGROUND

[0004] Color difference discrimination threshold refers to the minimum difference between two colors that can be distinguished by the human eye, and it is an important indicator for measuring the color sensitivity of the human eye, and is widely used in industries such as lighting industry, textile industry and printing industry.

[0005] The existing color difference discrimination threshold measurement method is usually to let the subjects compare whether two colors are the same or different under certain conditions, and then judge the color difference discrimination threshold of the subjects according to their responses. However, such measurement method is affected by the experimental setup, and the color difference discrimination thresholds obtained by using different experimental methods are not consistent, and the color difference formula in the traditional technology cannot accurately predict the minimum color difference that can be distinguished by the human eye. SUMMARY

[0006] Embodiments of the present application provide a method and device for evaluating chroma attribute, a computer device, a storage medium and a product.

[0007] In one aspect, the present application provides a method for evaluating chroma attribute, comprising: calculating a first color difference ΔE1 of a no-color-difference group under an illumination light source, the no-color-difference group being a combination of a standard sample and a no-color-difference product; calculating a second color difference ΔE2 of a to-be-evaluated group under the illumination light source, the to-be-evaluated group being a combination of the standard sample and a to-be-evaluated product; obtaining a false alarm probability of judging the no-color-difference group as "existing color difference" and a hit probability of judging the to-be-evaluated group as "existing color difference"; inputting the false alarm probability and the hit probability into a quantization model of the color difference discrimination threshold to obtain a color difference discrimination threshold of the to-be-evaluated product; inputting the false alarm probability and the hit probability into a quantization model of the color difference judgment tendency to obtain a judgment tendency estimation value of the to-be-evaluated product; and obtaining a chroma attribute evaluation result of the to-be-evaluated product based on the color difference discrimination threshold and the judgment tendency estimation value.

[0008] In a possible implementation, the method further includes: obtaining a spectral power distribution of the lighting source in the observation environment; and obtaining the spectral reflectance of the standard sample, the spectral reflectance of the non-color-difference product, and the spectral reflectance of the product to be evaluated; and wherein the obtained spectral power distribution, and the obtained spectral reflectance of the standard sample, the spectral reflectance of the non-color-difference product, and the spectral reflectance of the product to be evaluated are all used in the wavelength range.

[0009] In a possible implementation, the obtained spectral power distribution, and the obtained spectral reflectance of the standard sample, the spectral reflectance of the non-color-difference product, and the spectral reflectance of the product to be evaluated are all used in the wavelength range of 400 nm to 700 nm.

[0010] In a possible implementation, the method further includes: if the first color difference ΔE1 is within a first preset color difference range and the second color difference ΔE2 is within a second preset color difference range, constructing a quantification model of a color difference resolution threshold according to the first color difference ΔE1 and the second color difference ΔE2. In a possible implementation, the first preset color difference range is a ≤ ΔE1 ≤ b, where a = 0 and b = 0.15.

[0011] In a possible implementation, the quantification model of the color difference resolution threshold is:

[0012] wherein M1 is a color difference resolution threshold of the product to be evaluated, z() is an inverse function operation of a standard normal cumulative distribution function, HR i is a hit probability of the i th subject in the N subjects judging that the product to be evaluated has color difference, and FR i is a false alarm probability of the i th subject in the N subjects judging that the non-color-difference product has color difference.

[0013] In a possible implementation, the method further includes: if the first color difference ΔE1 is within a first preset color difference range and the second color difference ΔE2 is within a second preset color difference range, constructing an estimation model of a color difference judgment tendency according to the first color difference ΔE1.

[0014] In a possible implementation, the second preset color difference range is c ≤ ΔE2 ≤ d, where c = 0.30 and d = 2.50.

[0015] In a possible implementation, the estimation model of the color difference judgment tendency is:

[0016] wherein M2 is an estimated value of a color difference judgment tendency of the product to be evaluated.

[0017] In an aspect, an embodiment of the present application provides a color attribute evaluation device, comprising: a color difference calculation unit configured to calculate a first color difference ΔE1 of a colorless difference group under an illumination light source, the colorless difference group being a combination of a standard sample and a colorless difference product; calculate a second color difference ΔE2 of a to-be-evaluated group under the illumination light source, the to-be-evaluated group being a combination of the standard sample and a to-be-evaluated product; a color evaluation unit configured to obtain a false alarm probability of judging the colorless difference group as "existing color difference", and a hit probability of judging the to-be-evaluated group as "existing color difference"; input the false alarm probability and the hit probability into a quantification model of a color difference resolution threshold to obtain a color difference resolution threshold of the to-be-evaluated product; input the false alarm probability and the hit probability into a quantification model of a color difference judgment tendency to obtain a judgment tendency estimation value of the to-be-evaluated product; and obtain a color attribute evaluation result of the to-be-evaluated product based on the color difference resolution threshold and the judgment tendency estimation value.

[0018] In an aspect, an embodiment of the present application provides a computer device, comprising: a storage device and a processor; a memory, the memory storing one or more computer programs; and the processor being configured to load the one or more computer programs to implement the color attribute evaluation method described above.

[0019] In an aspect, an embodiment of the present application provides a computer readable storage medium, the computer readable storage medium storing a computer program, the computer program being adapted to be loaded by a processor and execute the color attribute evaluation method described above.

[0020] In an aspect, an embodiment of the present application provides a computer program product, the computer program product comprising a computer program, the computer program being adapted to be loaded by a processor and execute the color attribute evaluation method described above. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present specification or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present specification, and those skilled in the art can also obtain other drawings according to these drawings without any creative effort.

[0022] FIG. 1 is a flow diagram of a color attribute evaluation method provided by an embodiment of the present specification;

[0023] FIG. 2 is a structural diagram of a color attribute evaluation device provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0024] The exemplary embodiments will be described in detail below with reference to the accompanying drawings. In the following description, unless otherwise indicated, like numbers in the different drawings represent the same or similar elements. The following exemplary embodiments described are not meant to represent all implementations consistent with the present application. Rather, they are simply examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0025] Color difference discrimination threshold refers to the minimum difference between two colors that can be distinguished by the human eye, which is an important indicator of the sensitivity of the human eye to color, and has wide application in the textile industry, printing industry, product quality control, etc.

[0026] The measurement method of color difference discrimination threshold is usually to let the subjects compare whether two colors are the same or different under certain conditions, and then judge the color difference discrimination threshold according to the subject's response. However, this method has some problems, for example, the judgment tendency of the subject will affect the measurement result of the color difference discrimination threshold. Judgment tendency refers to the tendency of the subject to make the same or different judgment in uncertain situations, which is related to the subject's personality, psychological state, motivation, etc.

[0027] Since the judgment tendency of the subject is also affected by the experimental setup, the color difference discrimination threshold obtained by different experimental methods is not consistent, and the existing color difference formula cannot accurately predict the minimum color difference that can be distinguished by the human eye.

[0028] Therefore, in order to solve the above technical problems, the present application provides a color attribute evaluation method, which can simultaneously measure and quantify the color difference discrimination threshold and judgment tendency of the subject, thereby improving the measurement accuracy and reliability of the color difference discrimination threshold, and further providing guidance for light source quality evaluation and control optimization in the lighting industry application field.

[0029] As shown in FIG. 1, the present application provides a color attribute evaluation method, which realizes the prediction and evaluation of the color attribute of the human eye judgment of the measured object by constructing a quantitative model of color difference discrimination threshold and an estimation model of color difference judgment tendency respectively, wherein the color difference discrimination threshold and the color difference judgment tendency correspond to two parameters introduced in the signal detection theory: sensitivity and judgment standard. Sensitivity refers to the ability of the subject to distinguish between signal and noise; judgment standard refers to the decision threshold used by the subject when making a judgment.

[0030] It should be noted that the signal detection theory is a psychophysical model for describing the decision-making of human perception system or sensor in uncertain conditions. In the color discrimination process, the signal detection theory can divide the judgment results of the subjects into four cases: hit (correctly judging two different colors as having color difference), false negative (incorrectly judging two different colors as having no color difference), false positive (incorrectly judging two same colors as having color difference), and correct negative (correctly judging two same colors as having no color difference).

[0031] Specifically, the color attribute evaluation method in the present application is used to realize color difference discrimination threshold determination based on the signal detection theory, and the method comprises:

[0032] S100: calculating a first color difference ΔE1 of a non-color difference group under an illumination light source, the non-color difference group being a combination of a standard sample and a non-color difference product;

[0033] S200: calculating a second color difference ΔE2 of a to-be-evaluated group under the illumination light source, the to-be-evaluated group being a combination of the standard sample and a to-be-evaluated product;

[0034] Wherein, the standard sample is used as a reference, and the color value thereof is a standard color value with a constant value or in a constant range; the non-color difference product is a product meeting the color specification; and the to-be-evaluated product is a product to be judged whether meeting the color specification.

[0035] In the embodiments of the present application, the standard color block, the non-color difference color block and the to-be-evaluated color block are selected as the standard sample, the non-color difference product and the to-be-evaluated product respectively to form the non-color difference color block group and the to-be-evaluated color block group, and the color attribute evaluation test is completed through experiments to realize the characterization of the subjective visual discrimination ability of different colors.

[0036] The first color difference ΔE1 and the second color difference ΔE2 of the non-color difference color block group and the to-be-evaluated color block group under the illumination light source are calculated. Since the DE2000 color difference formula has high precision, the first color difference ΔE1 and the second color difference ΔE2 with high precision can be obtained.

[0037] In some other embodiments, the first color difference ΔE1 and the second color difference ΔE2 can also be calculated by using the color difference formula of the CIELAB color space.

[0038] In an implementation, the color attribute evaluation method of the present application further comprises:

[0039] acquire a spectral power distribution of an illuminating light source in an observation environment; and acquire a spectral reflectance of a standard sample, a spectral reflectance of a non-color-difference product, and a spectral reflectance of a product to be evaluated; wherein the acquired spectral power distribution, and the acquired spectral reflectance of the standard sample, the spectral reflectance of the non-color-difference product, and the spectral reflectance of the product to be evaluated are subjected to band information in a same wavelength range.

[0040] In the embodiments of the present application, six color blocks to be evaluated are used as experimental objects, five LED light sources with the same color temperature (5500K) and different Duv characteristics are used as illuminating light sources, and a constant stimulus psychophysics experiment method with five grades of scoring is used to explain the color difference discrimination threshold determination method based on the signal detection theory.

[0041] The different Duv characteristics correspond to Duv=-0.02, Duv=-0.01, Duv=0, Duv=+0.01, and Duv=+0.02, respectively.

[0042] It should be noted that the technical solutions of the present application are not limited to the above-mentioned illuminating light sources, and are also applicable to other LED illuminating light sources.

[0043] In an embodiment, the acquired spectral power distribution, and the acquired spectral reflectance of the standard sample, the spectral reflectance of the non-color-difference product, and the spectral reflectance of the product to be evaluated are all subjected to band information with wavelengths of 400nm-700nm.

[0044] By using the above-mentioned illuminating light source, natural light can be further simulated to eliminate the differences in color difference judgment of different human eyes under natural light through data processing.

[0045] Specifically, calculating the first color difference ΔE1 of the non-color-difference color block group under the illuminating light source further comprises: calculating the first color difference ΔE1 of the non-color-difference color block group under the illuminating light source based on the spectral power distribution of the illuminating light source, the spectral reflectance of the standard color block, and the spectral reflectance of the non-color-difference color block.

[0046] Calculating the second color difference ΔE2 of the color block group to be evaluated under the illuminating light source further comprises: calculating the second color difference ΔE2 of the color block group to be evaluated under the illuminating light source based on the spectral power distribution of the illuminating light source, the spectral reflectance of the standard color block, and the spectral reflectance of the color block to be evaluated.

[0047] In actual application scenarios, the spectral reflectance of the experimental object can be acquired by using a multi-spectral reflectance reconstruction method. The multi-spectral reflectance reconstruction method is a conventional technology, and will not be described here.

[0048] In actual application scenarios, a reflectance acquisition instrument can be used to obtain the spectral reflectance of the experimental object.

[0049] For ease of understanding, please refer to Table 1, which is the chroma attribute and DE2000 color difference of the standard color block, the non-color-difference color block and the color block to be evaluated under the illumination light source in this embodiment. The calculation results of color difference ΔE1 and color difference ΔE2 obtained by using the DE2000 color difference formula are shown in Table 1.

[0050] Table 1

[0051] Wherein, L, a, b are respectively the CIELAB color space chroma values of the color block under a specific light source.

[0052] In an embodiment, the chroma attribute evaluation method of the present application further comprises:

[0053] If the first color difference ΔE1 is within the first preset color difference range and the second color difference ΔE2 is within the second preset color difference range, a quantitative model of the color difference discrimination threshold is constructed according to the first color difference ΔE1 and the second color difference ΔE2.

[0054] If the first color difference ΔE1 is not within the first preset color difference range or the second color difference ΔE2 is not within the second preset color difference range, the evaluation method of the present application is not applicable.

[0055] Therefore, the first color difference ΔE1 and the second color difference ΔE2 that are not within the appropriate range are excluded from being used to construct the quantitative model of the color difference discrimination threshold and the estimation model of the color difference judgment tendency, so that the quantitative model of the color difference discrimination threshold and the estimation model of the color difference judgment tendency are accurate and reliable.

[0056] Wherein, the first preset color difference range is a≤ΔE1≤b, wherein a=0 and b=0.15. The color difference within this range is not easily distinguished by the human eye, greatly improving the accuracy of the quantitative model of the color difference discrimination threshold and the estimation model of the color difference judgment tendency.

[0057] It should be understood that the above-mentioned first preset color difference range is a relatively optimal range for the DE2000 color difference formula. When other color difference formulas are used, each color difference formula has its own corresponding first preset color difference range, that is, the upper and lower limits of the first preset color difference range have relevance with the color difference formula used.

[0058] In an embodiment, the chroma attribute evaluation method of the present application further comprises:

[0059] If the first color difference ΔE1 is within a first preset color difference range and the second color difference ΔE2 is within a second preset color difference range, an estimation model of color difference judgment tendency is constructed according to the first color difference ΔE1.

[0060] wherein the second preset color difference range is c≤ΔE2≤d, wherein c=0.30 and d=2.50. The color difference within the range is not easily distinguished by the human eye, greatly improving the accuracy of the quantification model of the color difference discrimination threshold.

[0061] It should be understood that the first preset color difference range described above is a relatively optimal range for the DE2000 color difference formula. When other color difference formulas are used, each color difference formula has a corresponding first preset color difference range, i.e., the upper and lower limits of the first preset color difference range are associated with the color difference formula used.

[0062] Thus, the judgment process of the first color difference ΔE1 and the second color difference ΔE2 is further advanced, and the subsequent steps can be terminated in time when an unsuitable first color difference ΔE1 and second color difference ΔE2 occurs, thereby improving efficiency and reducing resource waste.

[0063] According to the color difference ΔE1, the color difference ΔE2, and the acquisition results of the N subjects, a quantification model of the color difference discrimination threshold is constructed to obtain an estimated value of the color difference discrimination threshold for the color block to be evaluated.

[0064] Specifically, the quantification model of the color difference discrimination threshold is:

[0065] wherein M1 is the color difference discrimination threshold of the product to be evaluated, z() is the inverse function operation of the standard normal cumulative distribution function, HR i is the hit probability of the i-th subject in the N subjects judging the color difference as "existing" for the color difference group to be evaluated, FR i is the false alarm probability of the i-th subject in the N subjects judging the no-color-difference group as "existing" color difference.

[0066] When N is greater than or equal to 30, a more accurate quantification model of the color difference discrimination threshold and an estimation model of the color difference judgment tendency can be constructed by collecting sufficient acquisition samples, further improving the accuracy and reliability of the color difference discrimination threshold.

[0067] In this embodiment, a constant stimulus 5-level scoring method is used to conduct psychophysical experiments on 30 subjects, i.e., N=30.

[0068] According to the color difference ΔE1 and the acquisition results of the N subjects, an estimation model of the color difference judgment tendency is constructed to obtain an estimated value of the color difference judgment tendency for the color block to be evaluated.

[0069] Specifically, the estimation model of color difference judgment tendency is:

[0070] Wherein, M2 is the estimated value of the color difference judgment tendency of the product to be evaluated.

[0071] S300: obtaining the false alarm probability of judging the color difference group as "existing color difference" and the hit probability of judging the to-be-evaluated group as "existing color difference";

[0072] Specifically, the hit probability and the false alarm probability can be obtained by using constant stimulus on N subjects through psychophysical experiment, and by using whether judgment method or grading method.

[0073] S400: inputting the false alarm probability and the hit probability into the quantification model of the color difference resolution threshold to obtain the color difference resolution threshold of the product to be evaluated;

[0074] S500: inputting the false alarm probability and the hit probability into the quantification model of the color difference judgment tendency to obtain the estimated value of the judgment tendency of the product to be evaluated;

[0075] In the embodiment of the present application, by inputting the false alarm probability and the hit probability into the quantification model of the color difference resolution threshold, the color difference resolution threshold of the to-be-evaluated color block is obtained, and by inputting the false alarm probability and the hit probability into the quantification model of the color difference judgment tendency, the estimated value of the judgment tendency of the to-be-evaluated color block is obtained, which realizes the characterization of the subjective visual resolution ability of different colors, and further realizes the simultaneous acquisition and quantification of the color difference resolution threshold and the judgment tendency of the subjects, and through data processing, the difference in color difference judgment of different eyes in the same environment light, especially natural light, is eliminated, thereby improving the accuracy and reliability of the acquisition of the color difference resolution threshold, and realizing the accurate calibration and control of the color of structural parts and the color of printed matter in printing industry.

[0076] To further prove the technical advantages of the present application in the quantification of color difference resolution threshold and judgment tendency, the following will be described with some actual detection data.

[0077] The color difference resolution threshold quantification model and the color difference judgment tendency quantification model constructed in the embodiment of the present application are used to calculate the corresponding color difference resolution threshold M1 and the color difference judgment tendency estimated value M2 of the observer, and the calculation results are shown in Table 2.

[0078] Table 2: Normalized results of subjective evaluation of observers

[0079] The color difference resolution threshold M1 and the color difference judgment tendency estimation value M2 of the 30 observers are obtained through the above calculation.

[0080] The color difference resolution threshold estimation value M1 corresponding to each of the 30 subjects is averaged to obtain an updated M1. The updated M1 value represents the predicted resolvable color difference threshold of the corresponding color block group under a specific light source.

[0081] The color difference resolution threshold estimation value M1 corresponding to each of the 30 subjects is averaged to obtain an updated M1. The updated M1 value represents the predicted resolvable color difference threshold of the corresponding color block group under a specific light source.

[0082] It can be seen that the above results demonstrate that the present application has strong technical advantages in quantifying the color difference resolution threshold.

[0083] S600: Based on the color difference resolution threshold and the judgment tendency estimation value, the color attribute evaluation result of the product to be evaluated is obtained.

[0084] For example, under light source #1, the color difference resolution threshold of each of the 6 color block groups to be evaluated is in the range of 1.868-2.185 DE2000 units of color difference, i.e., the evaluation method in the present application can quantitatively estimate that the human eye color resolution threshold under the lighting conditions of light source #1 is about 2.0 color difference.

[0085] In other words, when the DE2000 color difference value of the color block group to be evaluated is greater than or equal to the M1 value, it is determined that the observer can recognize the color difference of the color block group to be evaluated under the current lighting conditions, i.e., the quantitative evaluation of the color attribute of the product to be evaluated can be estimated.

[0086] On the other hand, M2 represents the tendency of the subject to judge whether there is a color difference under specific experimental conditions. When the absolute value of M2 is equal to 0, it means that there is no color difference judgment tendency; the greater the absolute value of M2, the greater the color difference judgment tendency.

[0087] The present application also provides a quality inspection method using the above color attribute evaluation method, comprising:

[0088] M items are sampled to obtain N test items, wherein the M items have the same color to be inspected, and M and N are natural numbers, and N≤M;

[0089] N pieces of to-be-tested objects are taken as the to-be-evaluated color blocks in the color difference resolution threshold determination method, and the color difference resolution threshold determination method is used to obtain the color difference resolution threshold of the to-be-evaluated color blocks and the judgment tendency estimation value of the to-be-evaluated color blocks.

[0090] Based on the color difference resolution threshold of the to-be-evaluated color blocks and the judgment tendency estimation value of the to-be-evaluated color blocks, it is determined whether the to-be-inspected colors of the M pieces of objects exist in the color difference under the visual system.

[0091] In some actual application scenarios, the objects and the to-be-inspected colors of the objects can be magazines and colors to be detected on the magazines, or can be part colors of lamps.

[0092] In addition, in some actual application scenarios, the M pieces of objects can be a batch or several batches of mass products, and the N pieces of to-be-tested objects are corresponding samples for inspection in the batch or several batches of mass products, so that the quality control of the mass products is realized, and the resources and time required for quality control are saved.

[0093] In the inspection method of the embodiment, the color difference resolution threshold of the to-be-evaluated color blocks and the judgment tendency estimation value of the to-be-evaluated color blocks are obtained by using the color difference resolution threshold determination method, so that it can be accurately determined whether the sampled batch or several batches of objects exist in the color difference under the visual system, and thus accurate calibration and control of the colors of structural parts and the colors of printed matter in the printing industry can be realized.

[0094] In particular, when the parts of the lamps are lamp covers or PCB boards for light control and light guide, the parts will affect the light efficiency of the lamps, and thus the above inspection method can also be used to control whether the light efficiency of the lamps meets the standard.

[0095] Referring to FIG. 2, FIG. 2 is a structural schematic diagram of a color attribute evaluation device provided in the embodiment. Specifically, the color attribute evaluation device 200 can specifically include:

[0096] The color difference calculation unit 201 is configured to calculate a first color difference ΔE1 of a no-color-difference group under an illumination light source, the no-color-difference group being a combination of a standard sample and a no-color-difference product;

[0097] The color difference calculation unit 201 is configured to calculate a first color difference ΔE1 of a no-color-difference group under an illumination light source, the no-color-difference group being a combination of a standard sample and a no-color-difference product;

[0098] The color attribute evaluation unit 202 is configured to obtain a false alarm probability of judging the no-color-difference group as "existing color difference" and a hit probability of judging the to-be-evaluated group as "existing color difference".

[0099] input the false alarm probability and the hit probability into a quantification model of the color difference discrimination threshold, to obtain the color difference discrimination threshold of the product to be evaluated;

[0100] input the false alarm probability and the hit probability into a quantification model of the color difference discrimination threshold, to obtain the color difference discrimination threshold of the product to be evaluated;

[0101] based on the color difference discrimination threshold and the judgment tendency estimation value, obtain the colorimetric attribute evaluation result of the product to be evaluated.

[0102] The embodiment of the present application provides a computer device, which comprises a storage device and a processor.

[0103] a memory, wherein one or more computer programs are stored in the memory;

[0104] the processor is configured to load the one or more computer programs to implement the colorimetric attribute evaluation method in the present application.

[0105] In addition, it should be noted that the embodiment of the present application also provides a computer storage medium, and the computer storage medium stores a computer program, and the computer program comprises program instructions. When the processor executes the program instructions, the method in the foregoing embodiment can be executed, and therefore, the description will not be repeated here.

[0106] For technical details of the computer storage medium embodiment not disclosed in the present application, please refer to the description of the method embodiment of the present application. As an example, the program instructions can be deployed on one computer device, or executed on multiple computer devices located in one place, or executed on multiple computer devices distributed in multiple places and interconnected through a communication network.

[0107] According to one aspect of the present application, the embodiment of the present application further provides a computer program product or a computer program, which comprises computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device can execute the method in the foregoing embodiment, and therefore, the description will not be repeated here.

[0108] Those skilled in the art can be aware that units and algorithm steps of each example described in combination with the embodiments disclosed in the application can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on specific applications and design constraints of the technical solutions. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0109] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. Computer instructions can be stored in or transmitted by a computer-readable storage medium. Computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (for example, coaxial cable, optical fiber, digital line (DSL)) or wireless (for example, infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that the computer can access or a data processing device such as a server, data center, etc. integrated with one or more available media. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)) and the like.

[0110] The above disclosure is only the preferred embodiment of the application, and of course cannot be used to limit the scope of the application, therefore the equivalent changes made according to the claims of the application still fall within the scope of the application.

Claims

1. A method for evaluating chroma attributes, comprising: calculating a first color difference ΔE1 of a non-chroma-difference group under an illuminant, the non-chroma-difference group being a combination of a standard sample and a non-chroma-difference product; calculating a second color difference ΔE2 of an evaluation group under the illuminant, the evaluation group being a combination of the standard sample and an evaluation product; obtaining a false alarm probability of judging the non-chroma-difference group as "having color difference" and a hit probability of judging the evaluation group as "having color difference"; inputting the false alarm probability and the hit probability into a quantification model of a color difference discrimination threshold to obtain a color difference discrimination threshold of the evaluation product; inputting the false alarm probability and the hit probability into a quantification model of a color difference judgment tendency to obtain a judgment tendency estimation value of the evaluation product; and obtaining an evaluation result of chroma attributes of the evaluation product based on the color difference discrimination threshold and the judgment tendency estimation value. Further comprising: obtaining a spectral power distribution of the illuminant in an observation environment; and obtaining a spectral reflectance of the standard sample, a spectral reflectance of the non-chroma-difference product, and a spectral reflectance of the evaluation product; wherein the obtained spectral power distribution, and the obtained spectral reflectance of the standard sample, the spectral reflectance of the non-chroma-difference product, and the spectral reflectance of the evaluation product all use wavelength band information in the same wavelength range. The obtained spectral power distribution, and the obtained spectral reflectance of the standard sample, the spectral reflectance of the non-chroma-difference product, and the spectral reflectance of the evaluation product all use wavelength band information in the wavelength range of 400 nm-700 nm. Further comprising: constructing a quantification model of a color difference discrimination threshold according to the first color difference ΔE1 and the second color difference ΔE2 if the first color difference ΔE1 is within a first preset color difference range and the second color difference ΔE2 is within a second preset color difference range. The first preset color difference range is a≤ΔE1≤b, wherein a=0 and b=0.

15. Further comprising: constructing an estimation model of a color difference judgment tendency according to the first color difference ΔE1 if the first color difference ΔE1 is within a first preset color difference range and the second color difference ΔE2 is within a second preset color difference range. The second preset color difference range is c≤ΔE2≤d, wherein c=0.30 and d=2.

50.

2. The colorimetry attribute evaluation method of claim 1, wherein, Wherein, M2 is an estimation value of a color difference judgment tendency of the evaluation product. 10.A device for evaluating chroma attributes, comprising: a color difference calculation unit configured to calculate a first color difference ΔE1 of a non-chroma-difference group under an illuminant, the non-chroma-difference group being a combination of a standard sample and a non-chroma-difference product; calculate a second color difference ΔE2 of an evaluation group under the illuminant, the evaluation group being a combination of the standard sample and an evaluation product; a chroma evaluation unit configured to obtain a false alarm probability of judging the non-chroma-difference group as "having color difference" and a hit probability of judging the evaluation group as "having color difference"; input the false alarm probability and the hit probability into a quantification model of a color difference discrimination threshold to obtain a color difference discrimination threshold of the evaluation product; input the false alarm probability and the hit probability into a quantification model of a color difference judgment tendency to obtain a judgment tendency estimation value of the evaluation product; and obtain an evaluation result of chroma attributes of the evaluation product based on the color difference discrimination threshold and the judgment tendency estimation value.

3. The colorimetric property evaluation method of claim 2, wherein, ​ 4. The colorimetric property evaluation method of claim 1, wherein, ​ ​ 5. The colorimetric property evaluation method of claim 4, wherein, ​ 6. The colorimetric property evaluation method of claim 4, wherein, The quantification model of the color difference resolution threshold is: wherein M1 is the color difference discrimination threshold of the product to be evaluated, z() is the inverse function operation of the standard normal cumulative distribution function, HR i is the hit probability of the i-th subject among N subjects judging that the color difference exists in the group to be evaluated, FR i is the false alarm probability of the i-th subject among N subjects judging that the color difference exists in the group without color difference.

7. The colorimetric property evaluation method of claim 6, wherein, ​ ​ 8. The colorimetric property evaluation method of claim 7, wherein, ​ 9. The colorimetric property evaluation method of claim 7, wherein, The color difference judgment tendency estimation model is: ​ ​ ​ ​ ​ ​ inputting the false alarm probability and the hit probability into a quantification model of the color difference judgment tendency to obtain a judgment tendency estimation value of the product to be evaluated; obtaining a color attribute evaluation result of the product to be evaluated based on the color difference resolution threshold and the judgment tendency estimation value.

11. A computer device comprising: a storage device and a processor; a memory, the memory storing one or more computer programs; a processor configured to load the one or more computer programs to implement the color attribute evaluation method according to any one of claims 1-9.

12. A computer readable storage medium storing a computer program, the computer program being adapted to be loaded by a processor and executed to implement the color attribute evaluation method according to any one of claims 1-9.

13. A computer program product comprising a computer program, the computer program being adapted to be loaded by a processor and executed to implement the color attribute evaluation method according to any one of claims 1-9.

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