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

Abstract

The invention provides a chromaticity attribute evaluation method and device, computer equipment, a storage medium and a product, and the method comprises the steps: calculating a first chromatic aberration delta E1 of a chromatic aberration-free group under a lighting source, the chromatic aberration-free group being a combination of a standard sample and a chromatic aberration-free product; calculating a second chromatic aberration delta E2 of a to-be-evaluated group under the illumination light source, wherein the to-be-evaluated group is a combination of the standard sample and the to-be-evaluated product; acquiring a false alarm probability that the group without chromatic aberration is judged to have chromatic aberration and a hit probability that the group to be evaluated is judged to have chromatic aberration; inputting the false alarm probability and the hit probability into a quantitative model of the color difference resolution threshold to obtain the color difference resolution threshold of the to-be-evaluated product; inputting the false alarm probability and the hit probability into a quantitative model of the color difference judgment tendency to obtain a judgment tendency estimated value of the to-be-evaluated product; and obtaining a chromaticity attribute evaluation result of the to-be-evaluated product based on the color difference resolution threshold and the judgment tendency estimator value. According to the method, the color difference resolution threshold can be quantified, a general evaluation standard suitable for completing color judgment on different to-be-detected objects such as structural parts, printed matters and textiles is established, the reliability of the color difference resolution threshold is improved, and control over the quality of the to-be-detected objects is facilitated.

Description

Technical Field

[0001] The present disclosure relates to the field of color discrimination technology, and in particular to a method, apparatus, computer equipment, storage medium, and product for evaluating colorimetric properties. Background Art

[0002] The color difference discrimination threshold refers to the minimum difference between two colors that the human eye can distinguish. It is an important indicator to measure the human eye's sensitivity to color and is widely used in industries such as lighting, textile, and printing.

[0003] Existing methods for measuring color difference discrimination thresholds typically involve having subjects compare two colors under certain conditions to determine whether they are the same or different. The threshold is then determined based on the subjects' responses. However, this measurement method is affected by the experimental setup, and the color difference discrimination thresholds obtained using different experimental methods are inconsistent. Furthermore, existing color difference formulas cannot accurately predict the minimum color difference that the human eye can distinguish. Summary of the Invention

[0004] The embodiments of the present application propose a colorimetric attribute evaluation method, apparatus, computer equipment, storage medium, and product, which can quantify the color difference discrimination threshold, establish a universal evaluation standard suitable for completing color judgment on different test objects such as structural parts, printed materials, and textiles, improve the reliability of the color difference discrimination threshold, and facilitate the control of the quality of the test objects.

[0005] In one aspect, an embodiment of the present application provides a method for evaluating colorimetric properties, comprising:

[0006] Calculating the first color difference ΔE1 of the color-difference-free group under the illumination light source, wherein the color-difference-free group is a combination of the standard sample and the color-difference-free product;

[0007] Calculating a second color difference ΔE2 of a group to be evaluated under the illumination light source, wherein the group to be evaluated is a combination of the standard sample and the product to be evaluated;

[0008] Obtain the false alarm probability of judging the group without color difference as "having color difference" and the hit probability of judging the group to be evaluated as "having color difference";

[0009] Inputting the false alarm probability and the hit probability into the quantitative model of the color difference discrimination threshold to obtain the color difference discrimination threshold of the product to be evaluated;

[0010] Inputting the false alarm probability and the hit probability into the quantitative model of the color difference judgment tendency to obtain an estimated value of the judgment tendency of the product to be evaluated;

[0011] Based on the color difference discrimination threshold and the judgment tendency estimation value, a colorimetric attribute evaluation result of the product to be evaluated is obtained.

[0012] In one possible implementation, the method further includes: obtaining the 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 product without color difference, and the spectral reflectance of the product to be evaluated; wherein the obtained spectral power distribution, and the obtained spectral reflectance of the standard sample, the spectral reflectance of the product without color difference, and the spectral reflectance of the product to be evaluated use band information in the same wavelength range.

[0013] In a possible implementation, the obtained spectral power distribution, as well as the obtained spectral reflectance of the standard sample, the spectral reflectance of the colorless product, and the spectral reflectance of the product to be evaluated all use wavelength band information between 400 nm and 700 nm.

[0014] In a possible implementation, the method further includes:

[0015] 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, a quantitative model of a color difference discrimination threshold is constructed according to the first color difference ΔE1 and the second color difference ΔE2.

[0016] In a possible implementation, the first preset color difference range is a≤ΔE1≤b, where a=0 and b=0.15.

[0017] In a possible implementation, the quantization model of the color difference resolution threshold is:

[0018]

[0019] Among them, M1 is the color difference discrimination threshold of the product to be evaluated, is the inverse function of the standard normal cumulative distribution function, HR i is the probability that the i-th subject among N subjects judges the group to be evaluated as “having color difference”, FR i is the false alarm probability that the i-th subject among the N subjects judges the group without color difference as "with color difference".

[0020] In a possible implementation, the method further includes:

[0021] 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 for color difference judgment tendency is constructed according to the first color difference ΔE1.

[0022] In a possible implementation, the second preset color difference range is c≤ΔE2≤d, where c=0.30 and d=2.50.

[0023] In a possible implementation, the estimation model for the color difference judgment tendency is:

[0024]

[0025] Wherein, M2 is the estimated value of the color difference judgment tendency of the product to be evaluated.

[0026] In one aspect, an embodiment of the present application provides a chromaticity attribute evaluation device, comprising:

[0027] Color difference calculation unit: used to calculate the first color difference ΔE1 of the color-free group under the illumination light source, wherein the color-free group is a combination of the standard sample and the color-free product;

[0028] Calculating a second color difference ΔE2 of a group to be evaluated under the illumination light source, wherein the group to be evaluated is a combination of the standard sample and the product to be evaluated;

[0029] Chromaticity evaluation unit: used to obtain the false alarm probability of judging the group without color difference as "having color difference", and the hit probability of judging the group to be evaluated as "having color difference";

[0030] Inputting the false alarm probability and the hit probability into the quantitative model of the color difference discrimination threshold to obtain the color difference discrimination threshold of the product to be evaluated;

[0031] Inputting the false alarm probability and the hit probability into the quantitative model of the color difference judgment tendency to obtain an estimated value of the judgment tendency of the product to be evaluated;

[0032] Based on the color difference discrimination threshold and the judgment tendency estimation value, a colorimetric attribute evaluation result of the product to be evaluated is obtained.

[0033] In one aspect, an embodiment of the present application provides a computer device, comprising: a storage device and a processor;

[0034] a memory storing one or more computer programs;

[0035] The processor is configured to load the one or more computer programs to implement the above-mentioned colorimetric attribute evaluation method.

[0036] On the one hand, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the above-mentioned colorimetric attribute evaluation method.

[0037] On the one hand, an embodiment of the present application provides a computer program product, which includes a computer program, and the computer program is suitable for being loaded by a processor and executing the above-mentioned colorimetric attribute evaluation method.

[0038] In an embodiment of the present application, the first color difference ΔE1 of the colorless group under the lighting source is first calculated, and the colorless group is a combination of the standard sample and the colorless product; the second color difference ΔE2 of the group to be evaluated under the lighting source is calculated, and the group to be evaluated is a combination of the standard sample and the product to be evaluated; then, the false alarm probability of judging the colorless group as "having color difference" and the hit probability of judging the group to be evaluated as "having color difference" are obtained; and the false alarm probability and the hit probability are input into the quantitative model of the color difference resolution threshold to obtain the color difference resolution threshold of the product to be evaluated; the false alarm probability and the hit probability are input into the quantitative model of the color difference judgment tendency to obtain the judgment tendency estimation value of the product to be evaluated; finally, based on the color difference resolution threshold and the judgment tendency estimation value, the chromaticity attribute evaluation result of the product to be evaluated is obtained. It can be seen that, on the one hand, the present application constructs a quantitative model of color difference judgment tendency based on the first color difference ΔE1 and the second color difference ΔE2, as well as a quantitative model of the judgment tendency estimation value, and introduces two signal detection theory parameters, false alarm probability and hit probability, into the model to quantify the color difference resolution threshold and judgment tendency of the subjects, eliminate the interference of different individual differences and psychological states on the color difference resolution results, and thus accurately and reliably predict the evaluation results of the human eye's judgment of the chromaticity properties of the object to be tested, so as to realize the evaluation and regulation optimization of the light source quality of the lighting scene. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions and advantages of the embodiments of this specification or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0040] Figure 1 A schematic diagram of a flow chart of a colorimetric attribute evaluation method provided in an embodiment of this specification;

[0041] Figure 2 This is a schematic diagram of the structure of a chromaticity attribute evaluation device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0042] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0043] The color difference discrimination threshold refers to the minimum difference between two colors that the human eye can distinguish. It is an important indicator to measure the human eye's sensitivity to color and is widely used in the textile industry, printing industry, product quality control and other fields.

[0044] Color difference discrimination thresholds are typically measured by having subjects compare two colors under certain conditions to determine whether they are the same or different. The subject's response is then used to determine their color difference discrimination threshold. However, this method has some issues, such as the subject's judgment bias, which can affect the measurement of the color difference discrimination threshold. Judgment bias refers to a subject's tendency to make judgments of similarity or difference under uncertainty, and is related to factors such as the subject's personality, psychological state, and motivation.

[0045] Since the subjects' judgment tendencies are also affected by the experimental settings, the color difference discrimination thresholds obtained using different experimental methods are not consistent. At the same time, the existing color difference formula cannot accurately predict the minimum color difference that the human eye can distinguish.

[0046] Therefore, in order to solve the above technical problems, this application proposes a chromaticity attribute evaluation method, which can simultaneously measure and quantify the color difference discrimination threshold and judgment tendency of the subjects, 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 field of lighting industry applications.

[0047] like Figure 1 As shown, the present application provides a method for evaluating chromaticity attributes, which quantitatively realizes the predictive evaluation of the human eye's judgment of the chromaticity attributes of the object to be tested by constructing a quantitative model of the color difference discrimination threshold and an estimation model of the color difference judgment tendency, respectively. The color difference discrimination threshold and the color difference judgment tendency correspond to two parameters introduced into the signal detection theory: sensitivity and judgment criterion. Sensitivity refers to the subject's ability to distinguish between signals and noise; the judgment criterion refers to the decision threshold adopted by the subject when making a judgment.

[0048] It should be noted that signal detection theory is a psychophysical model used to describe how human perceptual systems or sensors make decisions under uncertainty. In the color discrimination process, signal detection theory can categorize the subject's judgment results into four situations: hits (correctly judging two different colors as having a color difference), misses (incorrectly judging two different colors as having no color difference), false alarms (incorrectly judging two identical colors as having a color difference), and true negatives (correctly judging two identical colors as having no color difference).

[0049] Specifically, the colorimetric attribute evaluation method in this application is used to implement color difference resolution threshold determination based on signal detection theory, and the method includes:

[0050] S100: Calculating a first color difference ΔE1 of a color-difference-free group under an illumination source, wherein the color-difference-free group is a combination of a standard sample and a color-difference-free product;

[0051] S200: Calculating a second color difference ΔE2 of a group to be evaluated under the illumination light source, wherein the group to be evaluated is a combination of the standard sample and the product to be evaluated;

[0052] Among them, the standard sample serves as a reference object, and its chromaticity value is a standard chromaticity value with a constant value or a constant range; the product without color difference is the product that meets the chromaticity specifications, and the product to be evaluated is the product to be judged whether it meets the chromaticity specifications.

[0053] In the embodiment of the present application, standard color blocks, color blocks without color difference, and color blocks to be evaluated are selected as standard samples, color block without color difference products, and products to be evaluated, respectively, to form a color block group without color difference and a color block group to be evaluated, and experiments are conducted to complete the colorimetric attribute evaluation test to achieve the characterization of the subjective visual discrimination ability of different colors.

[0054] Specifically, the first color difference ΔE1 and the second color difference ΔE2 of the color patch group without color difference and the color patch group to be evaluated under the illumination light source are calculated. Due to the high accuracy of the DE2000 color difference formula, the first color difference ΔE1 and the second color difference ΔE2 can be obtained with high accuracy.

[0055] In some other embodiments, the first color difference ΔE1 and the second color difference ΔE2 may also be calculated using the color difference formula of the CI ELAB color space.

[0056] In one embodiment, the colorimetric attribute evaluation method of the present application further includes:

[0057] Obtaining the 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 product without color difference, and the spectral reflectance of the product to be evaluated; wherein the obtained spectral power distribution, and the obtained spectral reflectance of the standard sample, the spectral reflectance of the product without color difference, and the spectral reflectance of the product to be evaluated use band information in the same wavelength range.

[0058] In the embodiment 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 lighting sources, and a constant stimulus psychophysical experimental method with a five-level scoring is adopted to illustrate the color difference resolution threshold determination method based on signal detection theory proposed in this application.

[0059] The different Duv characteristics correspond to Duv=-0.02, Duv=-0.01, Duv=0, Duv=+0.01, and Duv=+0.02.

[0060] It should be noted that the technical solution of the present invention is not limited to the above-mentioned lighting source, but is also applicable to other LED lighting sources.

[0061] In one embodiment, the obtained spectral power distribution, the obtained spectral reflectance of the standard sample, the spectral reflectance of the colorless product, and the spectral reflectance of the product to be evaluated all use wavelength band information between 400 nm and 700 nm.

[0062] By adopting the above-mentioned lighting source, natural light can be further simulated, so that the differences in color difference judgment of different human eyes under natural light can be eliminated through data processing.

[0063] Specifically, calculating the first color difference ΔE1 of the achromatic color patch group under the illumination light source further includes: calculating the first color difference ΔE1 of the achromatic color patch group under the illumination light source based on the spectral power distribution of the illumination light source, the spectral reflectance of the standard color patch, and the spectral reflectance of the achromatic color patch.

[0064] Calculating the second color difference ΔE2 of the color patch group to be evaluated under the illumination light source further includes: calculating the second color difference ΔE2 of the color patch group to be evaluated under the illumination light source based on the spectral power distribution of the illumination light source, the spectral reflectance of the standard color patch, and the spectral reflectance of the color patch to be evaluated.

[0065] In practical application scenarios, the spectral reflectance of the experimental object can be obtained by multispectral reflectance reconstruction. The multispectral reflectance reconstruction method is an existing technology and will not be described in detail here.

[0066] In actual application scenarios, a reflectance acquisition instrument can be used to obtain the spectral reflectance of the experimental object.

[0067] For ease of understanding, please refer to Table 1, which shows the chromaticity properties and DE2000 color differences of the standard color block, the color block without color difference, and the color block to be evaluated under the lighting source in this embodiment. The calculation results of the color difference ΔE1 and the color difference ΔE2 obtained using the DE2000 color difference formula are shown in Table 1.

[0068] Table 1

[0069]

[0070]

[0071] Wherein, L, a, and b are the chromaticity values ​​of the corresponding color block in the CI ELAB color space under a specific light source.

[0072] In one embodiment, the colorimetric attribute evaluation method of the present application further includes:

[0073] 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, a quantitative model of a color difference discrimination threshold is constructed according to the first color difference ΔE1 and the second color difference ΔE2.

[0074] 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.

[0075] Therefore, it is eliminated to construct the quantitative model of the color difference resolution threshold and the estimation model of the color difference judgment tendency using the first color difference ΔE1 and the second color difference ΔE2 that are not within the appropriate range, thereby making the quantitative model of the color difference resolution threshold and the estimation model of the color difference judgment tendency accurate and reliable.

[0076] The first preset color difference range is a≤ΔE1≤b, where a=0 and b=0.15. Color differences within this range are difficult for the human eye to discern, greatly improving the accuracy of the quantitative model for the color difference discrimination threshold and the estimation model for the color difference judgment tendency.

[0077] It should be understood that the above-mentioned first preset color difference range is a preferred 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 are correlated with the color difference formula used.

[0078] In one embodiment, the colorimetric attribute evaluation method of the present application further includes:

[0079] 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 for color difference judgment tendency is constructed according to the first color difference ΔE1.

[0080] The second preset color difference range is c≤ΔE2≤d, where c = 0.30 and d = 2.50. Color differences within this range are difficult for the human eye to discern, which greatly improves the accuracy of the quantitative model of the color difference discrimination threshold.

[0081] It should be understood that the above-mentioned first preset color difference range is a preferred 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 are correlated with the color difference formula used.

[0082] Thus, the judgment process of the first color difference ΔE1 and the second color difference ΔE2 is further advanced, and subsequent steps can be terminated in time when inappropriate first color difference ΔE1 and second color difference ΔE2 appear, thereby improving efficiency and reducing resource waste.

[0083] According to the color difference ΔE1, the color difference ΔE2 and the results obtained from N subjects, a quantitative 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.

[0084] Specifically, the quantitative model of the color difference discrimination threshold is:

[0085]

[0086] Among them, M1 is the color difference discrimination threshold of the product to be evaluated, is the inverse function of the standard normal cumulative distribution function, HR i is the probability that the i-th subject among N subjects judges the group to be evaluated as “having color difference”, FR i is the false alarm probability that the i-th subject among the N subjects judges the group without color difference as “with color difference”.

[0087] When N is greater than or equal to 30, by collecting enough samples, a more accurate quantitative model of the color difference discrimination threshold and an estimation model of the color difference judgment tendency can be constructed, thereby further improving the accuracy and reliability of obtaining the color difference discrimination threshold.

[0088] In this embodiment, a psychophysical experiment was conducted on 30 subjects using a 5-level scoring method with constant stimulation, ie, N=30.

[0089] According to the color difference ΔE1 and the results obtained from 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.

[0090] Specifically, the estimation model of color difference judgment tendency is:

[0091]

[0092] Wherein, M2 is the estimated value of the color difference judgment tendency of the product to be evaluated.

[0093] S300: Obtaining the false alarm probability of judging the group without color difference as “with color difference” and the hit probability of judging the group to be evaluated as “with color difference”;

[0094] Specifically, the hit probability and false alarm probability can be obtained through a psychophysical experiment using constant stimulation on N subjects and through a yes / no judgment method or a graded scoring method.

[0095] S400: Inputting the false alarm probability and the hit probability into the quantitative model of the color difference discrimination threshold to obtain the color difference discrimination threshold of the product to be evaluated;

[0096] S500: Inputting the false alarm probability and the hit probability into the quantitative model of the color difference judgment tendency to obtain an estimated value of the judgment tendency of the product to be evaluated;

[0097] In an embodiment of the present invention, the color difference resolution threshold of the color block to be evaluated is obtained by inputting the false alarm probability and the hit probability into a quantitative model of the color difference discrimination threshold, and the judgment tendency estimation value of the color block to be evaluated is obtained by inputting the false alarm probability and the hit probability into a quantitative model of the color difference judgment tendency, thereby achieving the characterization of the subjective visual discrimination ability of different colors, and further, achieving the simultaneous acquisition and separate quantification of the color difference resolution threshold and judgment tendency of the subjects, and eliminating the differences in color difference judgment of different human eyes under the same ambient light, especially natural light, through data processing, thereby improving the accuracy and reliability of obtaining the color difference resolution threshold, and achieving accurate calibration and control of the color of structural parts, the color of printed materials in the printing industry, etc.

[0098] In order to further demonstrate the technical advantages of the technical solution of the present invention in terms of color difference resolution threshold and judgment tendency quantification, some actual detection data will be used for illustration below.

[0099] The color difference discrimination threshold M1 and the color difference judgment tendency estimation value M2 corresponding to the observer are calculated using the quantitative model of the color difference discrimination threshold and the quantitative model of the color difference judgment tendency constructed in the embodiment of the present invention. The calculation results are shown in Table 2 below.

[0100] Table 2 Normalized results of observers’ subjective evaluation

[0101]

[0102]

[0103] Through the above calculation, the color difference discrimination threshold M1 and the color difference judgment tendency estimation value M2 of the 30 observers can be obtained.

[0104] The average value of the color difference discrimination threshold estimation value M1 corresponding to each of the 30 subjects is taken to obtain the updated M1. The updated M1 value represents the predicted discernible color difference threshold of the corresponding color block group to be evaluated under a specific light source.

[0105] The updated M2 was obtained by averaging the estimated color difference judgment tendency values ​​M2 for each of the 30 subjects. M2 represents the subject's tendency to judge whether a color difference exists under specific experimental conditions. The above calculations also yielded estimates of the color difference judgment tendency for the 30 observers, which can characterize the shift in the observer's decision criteria under different experimental conditions.

[0106] It can be seen that the above results confirm that the present invention has strong technical advantages in the quantification of color difference resolution threshold.

[0107] S600: Based on the color difference discrimination threshold and the judgment tendency estimation value, obtain the color attribute evaluation result of the product to be evaluated.

[0108] For example, under light source #1, the color difference discrimination thresholds obtained based on the six color block groups to be evaluated are all within the range of 1.868 to 2.185 DE2000 unit color differences. That is, the evaluation method in this application can quantitatively estimate that the human eye's color discrimination threshold under the lighting conditions of light source #1 is approximately 2.0 color differences.

[0109] 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 under the corresponding current lighting conditions, the observer can identify the color difference of the color block group to be evaluated, that is, the estimated judgment of the colorimetric attribute evaluation of the product to be evaluated can be quantitatively realized.

[0110] On the other hand, M2 represents the subject's tendency to judge whether there is a color difference under specific experimental conditions. If its absolute value is 0, it means that there is no tendency to judge the color difference; the larger its absolute value, the greater the tendency to judge the color difference.

[0111] This application also provides a quality inspection method using the above-mentioned colorimetric attribute evaluation method, including:

[0112] Sample M items to obtain N items to be tested, where M items have the same color to be inspected, and M and N are both natural numbers, N≤M;

[0113] Taking N objects to be tested as the color blocks to be evaluated in the color difference discrimination threshold determination method described above, the color difference discrimination threshold and the judgment tendency estimation value of the color blocks to be evaluated are obtained by using the color difference discrimination threshold determination method described above;

[0114] Color difference discrimination threshold based on the color block to be evaluated and judgment tendency estimation of the color block to be evaluated + The color of the M items to be inspected is determined by the color value to determine whether there is color difference under the visual system.

[0115] In some practical application scenarios, the colors of items to be inspected can be the colors that need to be inspected in magazines, or the colors of parts of lamps.

[0116] In addition, in some practical application scenarios, the M items can be one or several batches of mass products, and the N items to be tested are correspondingly random inspection samples from one or several batches of mass products. In this way, the quality control of the mass products can be achieved while saving the resources and time required for quality control.

[0117] In the quality inspection method of this embodiment, by sampling and then using the color difference resolution threshold judgment method as described above to obtain the color difference resolution threshold of the color block to be evaluated and the judgment tendency estimation value of the color block to be evaluated, it is possible to accurately judge whether a batch or several batches of sampled items have color difference under the visual system, thereby achieving accurate calibration and control of the color of structural parts and the color of printed materials in the printing industry.

[0118] In particular, when the components of a lamp are a mask in the lamp, or a PCB board used for light control, light guidance, etc., since these components will affect the luminous efficiency of the lamp, the above-mentioned quality inspection method can also be used to control whether the luminous efficiency of the lamp meets the standard.

[0119] See also Figure 2 , Figure 2 2 is a schematic diagram of the structure of a chromaticity property evaluation device provided in an embodiment of the present application. In a specific implementation, the chromaticity property evaluation device 200 may specifically include:

[0120] Color difference calculation unit 201: used to calculate the first color difference ΔE1 of the color-difference-free group under the illumination light source, wherein the color-difference-free group is a combination of a standard sample and a color-difference-free product;

[0121] Calculating a second color difference ΔE2 of a group to be evaluated under the illumination light source, wherein the group to be evaluated is a combination of the standard sample and the product to be evaluated;

[0122] Chroma evaluation unit 202: used to obtain the false alarm probability of judging the group without color difference as "having color difference", and the hit probability of judging the group to be evaluated as "having color difference";

[0123] Inputting the false alarm probability and the hit probability into the quantitative model of the color difference discrimination threshold to obtain the color difference discrimination threshold of the product to be evaluated;

[0124] Inputting the false alarm probability and the hit probability into the quantitative model of the color difference judgment tendency to obtain an estimated value of the judgment tendency of the product to be evaluated;

[0125] Based on the color difference discrimination threshold and the judgment tendency estimation value, a colorimetric attribute evaluation result of the product to be evaluated is obtained.

[0126] An embodiment of the present application provides a computer device comprising: a storage device and a processor;

[0127] a memory storing one or more computer programs;

[0128] The processor is configured to load the one or more computer programs to implement the colorimetric attribute evaluation method of the present application.

[0129] In addition, it should be pointed out here 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 includes program instructions. When the processor executes the above program instructions, it can execute the method in the corresponding embodiment above. Therefore, it will not be repeated here.

[0130] For technical details not disclosed in the computer storage medium embodiments involved in this application, please refer to the description of the method embodiments of this application. As an example, the program instructions can be deployed on a computer device, or executed on multiple computer devices located in a single location, or executed on multiple computer devices distributed in multiple locations and interconnected by a communication network.

[0131] According to one aspect of the present application, embodiments of the present application further provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, enabling the computer device to perform the methods described in the corresponding embodiments above. Therefore, these methods will not be described in detail here.

[0132] Those skilled in the art will appreciate that the units and algorithmic steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0133] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented using 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 invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data processing device such as a server or data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0134] The above disclosure is only a preferred embodiment of the present application, and certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A method for evaluating colorimetric properties, characterized in that: include: Calculating the first color difference ΔE1 of the color-difference-free group under the illumination light source, wherein the color-difference-free group is a combination of the standard sample and the color-difference-free product; Calculating a second color difference ΔE2 of a group to be evaluated under the illumination light source, wherein the group to be evaluated is a combination of the standard sample and the product to be evaluated; Obtain the false alarm probability of judging the group without color difference as "with color difference" and the hit probability of judging the group to be evaluated as "with color difference"; Inputting the false alarm probability and the hit probability into the quantitative model of the color difference discrimination threshold to obtain the color difference discrimination threshold of the product to be evaluated; Inputting the false alarm probability and the hit probability into the quantitative model of the color difference judgment tendency to obtain an estimated value of the judgment tendency of the product to be evaluated; Based on the color difference discrimination threshold and the judgment tendency estimation value, a colorimetric attribute evaluation result of the product to be evaluated is obtained.

2. The colorimetric attribute evaluation method according to claim 1, wherein: Also includes: Obtaining the 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 product without color difference, and the spectral reflectance of the product to be evaluated; wherein the obtained spectral power distribution, and the obtained spectral reflectance of the standard sample, the spectral reflectance of the product without color difference, and the spectral reflectance of the product to be evaluated use band information in the same wavelength range.

3. The colorimetric attribute evaluation method according to claim 2, wherein: The obtained spectral power distribution, the obtained spectral reflectance of the standard sample, the spectral reflectance of the product without color difference and the spectral reflectance of the product to be evaluated all use wavelength band information between 400 nm and 700 nm.

4. The method for evaluating colorimetric properties according to claim 1, wherein: Also 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, a quantitative model of a color difference discrimination threshold is constructed according to the first color difference ΔE1 and the second color difference ΔE2.

5. The colorimetric attribute evaluation method according to claim 4, wherein: The first preset color difference range is a≤ΔE1≤b, where a=0 and b=0.

15.

6. The colorimetric attribute evaluation method according to claim 4, wherein: The quantitative model of the color difference discrimination threshold is: Among them, 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 probability that the i-th subject among N subjects judges the group to be evaluated as "having color difference", FR i is the false alarm probability that the i-th subject among the N subjects judges the group without color difference as "with color difference".

7. The method for evaluating chromaticity attributes according to claim 6, wherein: Also 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, an estimation model for color difference judgment tendency is constructed according to the first color difference ΔE1.

8. The method for evaluating chromaticity attributes according to claim 7, wherein: The second preset color difference range is c≤ΔE2≤d, where c=0.30 and d=2.

50.

9. The method for evaluating chromaticity attributes according to claim 7, wherein: The estimation model of the color difference judgment tendency is: Wherein, M2 is the estimated value of the color difference judgment tendency of the product to be evaluated.

10. A chromaticity attribute evaluation device, characterized in that: include: Color difference calculation unit: used to calculate the first color difference ΔE1 of the color-free group under the illumination light source, wherein the color-free group is a combination of the standard sample and the color-free product; Calculating a second color difference ΔE2 of a group to be evaluated under the illumination light source, wherein the group to be evaluated is a combination of the standard sample and the product to be evaluated; Chromaticity evaluation unit: used to obtain the false alarm probability of judging the group without color difference as "having color difference", and the hit probability of judging the group to be evaluated as "having color difference"; Inputting the false alarm probability and the hit probability into the quantitative model of the color difference discrimination threshold to obtain the color difference discrimination threshold of the product to be evaluated; Inputting the false alarm probability and the hit probability into the quantitative model of the color difference judgment tendency to obtain an estimated value of the judgment tendency of the product to be evaluated; Based on the color difference discrimination threshold and the judgment tendency estimation value, a colorimetric attribute evaluation result of the product to be evaluated is obtained.

11. A computer device, characterized in that: include: storage devices and processors; a memory storing one or more computer programs; A processor is configured to load the one or more computer programs to implement the colorimetric attribute evaluation method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the colorimetric property evaluation method according to any one of claims 1 to 9.

13. A computer program product, characterized in that The computer program product comprises a computer program adapted to be loaded by a processor and to execute the colorimetric property evaluation method according to any one of claims 1 to 9.

Images