A method and system for personalized color display of a display

Abstract

The present invention discloses a method and system for personalized color display of a display. The present invention aims to, when a user uses an electronic device with a display screen, find the most suitable color matching function of a classified observer in a database according to the estimated user characteristic color, and use the color matching function of this classified observer to replace the CIE standard color matching function to achieve personalized color reproduction. The present invention can ensure that different users have personalized color displays on a display device, achieving the purpose of accurate color display and personalization, and providing accurate color reproduction for users.

Description

Technical Field

[0001] The present invention belongs to the technical field of display screen color management, and particularly relates to a personalized color display method and system based on user characteristic color estimation and classification of color vision observers. Background Art

[0002] Due to individual differences in macular and lens optical density, retinal photoreceptor pigment optical density, and spectral shift of cone photoreceptor pigments, the color matching function (CMF) of humans varies among different observers. These differences may cause two stimuli with different spectral power distributions (SPDs) to appear the same to one observer but different to another. This phenomenon is called observer metamerism (OM).

[0003] In the past, observer metamerism has been largely ignored because most spectra of light in nature and traditional display devices are relatively wide, so the response variations caused by different CMFs are relatively small. However, recent technological advancements in using narrow-spectrum primaries to produce large-gamut displays (such as LED, OLED, laser, and quantum dot displays) have led to observer metamerism becoming an increasingly serious problem, which results in serious color reproduction errors in the eyes of some users for electronic devices that reproduce colors using standard CIE color matching functions. To solve this problem and reduce color reproduction errors, the method is to use a color matching function closer to that of the user.

[0004] Recently, the applicant measured the color matching functions of 100 people aged from 10 to 80 years old, including two field angles of 2° and 10°, for a total of 200 color matching functions. Existing research shows that although there are huge differences in the color matching functions of people in the population, 10 classification observers can be used to represent the color vision of the vast majority of individuals in the population. Based on the 200 people's color matching functions, using the K-means clustering method, they are classified into 10 categories, and these 10 classification color matching functions are used to represent people's color vision. Users only need to determine which of the 10 color matching function categories they belong to through a simple experiment to achieve a personalized color solution.

[0005] The concept of "Unique Hue" originated in the 1960s. It refers to the pure colors that people perceive in their consciousness without reference, specifically including Unique Red, Unique Yellow, Unique Green, and Unique Blue. Unique Red and Unique Green are defined as the red and green hues without any traces of yellow and blue. Unique Yellow and Unique Blue are the yellow and blue hues without any traces of red and green. The unique color is related to many factors, including the scene when conducting the unique color experiment, whether the device used is a monitor or an object color sample. However, when all other scenes are fixed, what affects the unique color is the human color matching function curve. But for a specific person, when using different monitors or object color samples, the hue angle of the unique color in the color space may be different, but these colors have the same color appearance for this observer. The difference in the hue angle is metamerism caused by the difference between the standard CIE color matching function and the individual color matching function of the user. Since the reflection spectrum of the object color sample is relatively wide and the degree of metamerism is not large, the average unique color hue can be used to approximately replace the hue angle of the unique color when the user uses the object color sample. At this time, only one color matching function needs to be selected from the classified observers so that it can calculate the minimum deviation from the hue angle of the object color sample. Then this color matching function is the classified observer closest to this observer. Summary of the Invention

[0006] The object of the present invention is to solve the problem of color reproduction error caused by observer metamerism described in the background technology, and to propose a personalized color display method and system based on classified color vision observers and unique color estimation.

[0007] The specific technical solutions adopted by the present invention are as follows:

[0008] In a first aspect, the present invention provides a method for personalized color display of a monitor, which includes:

[0009] S1. Obtain all classified observer color matching functions; the classified observer color matching functions are pre-clustered based on a data set composed of color matching functions of different observers. During the clustering process, several CIE standard color matching functions are set as fixed clustering centers, and the remaining clustering centers are iteratively optimized based on the distance values between color matching functions. The distance value between any two color matching functions is the average standard color difference of these two color matching functions in cross-media color reproduction. Each clustering center after iteration to convergence is used as a classified observer color matching function;

[0010] S2. Obtain all user unique colors of the target user, where the type of unique color is memory color or unique hue;

[0011] S3. Convert the spectral power distribution of each user's characteristic color to the target color space based on each classification observer color matching function, and calculate the color difference between the converted user characteristic color coordinate values and the standard reference values of the user characteristic color; select the classification observer color matching function with the smallest average color difference among all user characteristic colors as the color matching function set for the display, and provide personalized color display for the target user.

[0012] Preferably, in the first aspect above, the color matching functions in the data set are XYZ color matching functions.

[0013] Preferably, in the first aspect above, the clustering uses k-means clustering, and the number of cluster centers is not less than 6.

[0014] Preferably, in the first aspect above, during the clustering process, set the CIE 2015 2° color matching function and the CIE 2015 10° color matching function as two fixed cluster centers.

[0015] Preferably, in the first aspect above, the average standard color difference uses the average CIEDE2000 color difference.

[0016] Preferably, in the first aspect above, the target color space is the CIELAB space. When converting the spectral power distribution of the user characteristic color to the target color space, it is necessary to calculate the tristimulus values corresponding to the spectral power of the user characteristic color and the white point respectively based on each classification observer color matching function, and then convert the user characteristic color tristimulus values to the CIELAB space with the white point tristimulus values as the reference benchmark.

[0017] Preferably, in the first aspect above, the method for obtaining the user characteristic color of the target user is: provide a GUI interaction interface for the characteristic color estimation experiment on the user's personal electronic device and record the characteristic color estimation results, and the characteristic color estimation experiment is a memory color experiment or a characteristic color tone matching experiment.

[0018] In a second aspect, the present invention provides a display personalized color display system, which includes:

[0019] A classification observer acquisition module for acquiring all classification observer color matching functions; the classification observer color matching functions are pre-clustered based on a data set composed of color matching functions of different observers. During the clustering process, several CIE standard color matching functions are set as fixed cluster centers, and the remaining cluster centers are iteratively optimized based on the distance values between the color matching functions. The distance value between any two color matching functions is the average standard color difference of these two color matching functions in cross-media color reproduction. Each cluster center after iteration to convergence is used as a classification observer color matching function;

[0020] A characteristic color acquisition module, configured to acquire all user characteristic colors of a target user, where the characteristic color type is a memory color or a characteristic color tone;

[0021] A personalized color display module, configured to convert the spectral power distribution of each user characteristic color to a target color space based on each classification observer color matching function, and calculate the color difference between the converted user characteristic color coordinate value and the standard reference value of the user characteristic color; select the classification observer color matching function with the smallest average color difference among all user characteristic colors as the color matching function set by the display, and provide personalized color display for the target user.

[0022] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for personalized color display of a display as described in any one of the above first aspects is implemented.

[0023] In a fourth aspect, the present invention provides a computer electronic device, which includes a memory and a processor;

[0024] The memory is used to store a computer program;

[0025] The processor is configured to, when executing the computer program, implement the method for personalized color display of a display as described in any one of the above first aspects.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] Due to the differences in color vision among different individuals, the present invention provides a method and system for personalized color display of a display, aiming to find the most suitable classification observer color matching function in a database according to the estimated user characteristic color when a user uses an electronic device with a display screen, that is, select a classification observer closest to the user from all classification observers, and use the color matching function of this classification observer to replace the CIE standard color matching function to achieve personalized color reproduction. Through the acquisition of user characteristic colors and the optimization of the search for color matching functions, the present invention enables different users to have personalized color displays on display devices, achieving the purpose of accurate color display and personalization, and providing accurate color reproduction for users. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flowchart of the steps of the method for personalized color display of a display according to the present invention;

[0029] Figure 2 It is an exemplary GUI interface display diagram of Method 1 for memory color matching in the present invention;

[0030] Figure 3 is an exemplary GUI interface display diagram for performing the memory color matching method 2 of the present invention;

[0031] Figure 4 is an exemplary GUI interface display diagram for performing the memory color evaluation method in the present invention;

[0032] Figure 5 This is an exemplary GUI interface display diagram of the characteristic hue forced selection experiment in the present invention;

[0033] Figure 6 It is a schematic diagram of the structure of computer electronic equipment;

[0034] Figure 7 is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the above-mentioned purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation mode of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The technical features in each embodiment of the present invention can be combined accordingly without conflicting with each other.

[0036] like Figure 1 As shown, in a preferred embodiment of the present invention, a display personalized color display method is provided, which includes:

[0037] S1. Obtain all classified observer color matching functions; the classified observer color matching functions are obtained in advance by clustering a data set composed of color matching functions of different observers. During the clustering process, several CIE standard color matching functions are set as fixed cluster centers, and the remaining cluster centers are iteratively optimized with distance values between color matching functions. The distance value between any two color matching functions is the average standard color difference of the two color matching functions in cross-media color reproduction, and each cluster center after iteration to convergence is regarded as a classified observer color matching function.

[0038] S2. Obtain all user characteristic colors of the target user, where the characteristic color type is a memory color or a characteristic hue;

[0039] S3. Convert the spectral power distribution of each user characteristic color to the target color space based on each classification observer color matching function, and calculate the color difference between the converted user characteristic color coordinate value and the standard reference value of the user characteristic color; select the classification observer color matching function with the smallest average color difference among all user characteristic colors as the color matching function set for the display to provide personalized color display for the target user.

[0040] It should be noted that in the above step S1, the method of obtaining all classification observer color matching functions needs to be understood in a broad sense. It can be either reading all the pre-clustered classification observer color matching functions from a data storage device or obtaining all classification observer color matching functions by reading and clustering a data set online. In practical applications, the classification observer color matching functions can be pre-clustered and then stored in the local end of the user's personal electronic device or in the cloud accessed by the user's personal electronic device to improve the usability and convenience of the present invention.

[0041] In an embodiment of the present invention, a specific method for clustering to obtain classification observer color matching functions is provided, which can be implemented according to the following two steps:

[0042] Step 101: Obtain a data set composed of color matching functions of different observers

[0043] The source of the color matching function data in the data set includes using real measured color matching function data or data generated by computer simulation. The real measured color matching function data can be the 49 color matching function data of Stiles & Burch and the color matching function data of 100 observers measured by the Luo Ming team of Zhejiang University (hereinafter referred to as Zhejiang data). The data generated by computer simulation is mainly generated by Asano et al. according to the distribution of physiological visual parameters in the population.

[0044] Taking the Zhejiang University data consisting of the color matching functions of 100 observers as an example, these color matching functions are given in the form of visual physiological parameters, which include lens pigment optical density (Lens OD), macular pigment optical density (MPOD), L-cone photopigment optical density (L OD), M-cone photopigment optical density (M OD), S-cone photopigment optical density (S OD), L-cone spectrashifts (L shifts), and M-cone spectra shifts (M shifts). Using the Stockman-extended CIEPO06 model, the physiological visual parameters can be converted into continuous LMS color matching functions. The CIEPO06 model is as follows:

[0045]

[0046] Thus, the process of obtaining continuous LMS color matching functions from 7 physiological visual outputs can be expressed by the following formula:

[0047]

[0048] The above conversion process can be summarized as: According to the optical densities of L, M, and S cones and the pigment absorption spectra calculate the absorption rates of L, M, and S cones, and multiply the cone absorption rates by the macular transmittance and the lens transmittance to obtain the response curves of L, M, and S cones respectively as a function of light wavelength 、 、。The LMS color matching functions can be linearly transformed into XYZ color matching functions. Different fields of view have different transformation matrices. The transformations of the 2-degree and 10-degree color matching functions are as follows:

[0049]

[0050]

[0051] ​Calculate the 2-degree and 10-degree physiological visual parameters of 100 observers according to the above calculation formula, and 200 XYZ color matching functions can be obtained. These XYZ color matching functions can be used to construct a data set.

[0052] Step 102: Cluster the color matching functions in the data set in Step 101.

[0053] Taking the 200 XYZ color matching functions obtained above as an example for clustering, the purpose of clustering is to optimally select some XYZ color matching functions from the data set as the clustering centers. The XYZ color matching functions corresponding to these clustering centers are the XYZ color matching functions of the standard colorimetric observer.

[0054] In the present invention, the clustering algorithm is not limited. Preferably, the k-means clustering algorithm can be used. Different from the traditional clustering algorithm, in the present invention, several CIE standard color matching functions need to be set as fixed and unchangeable clustering centers. These fixed clustering centers will not be optimized and changed, and the remaining clustering centers will be iteratively optimized by the clustering algorithm normally. The number of clustering centers can be optimized and adjusted according to the actual situation. Preferably, it is not less than 6, and further preferably 10, among which 2 clustering centers are fixedly adopted as the CIE standard color matching functions. Therefore, in the embodiment of the present invention, the CIE 2015 2° color matching function and the CIE 2015 10° color matching function can be added as fixed clustering centers to the data set containing 200 XYZ color matching functions, for a total of 202 XYZ color matching functions.

[0055] Perform k-means clustering on the data set composed of the above 202 color matching functions, set 10 clustering centers (centroids), and randomly select 8 out of the 10 clustering centers from the 200 XYZ color matching functions each time, and force the CIE2015 2° color matching function and the CIE 2015 10° color to be two fixed color matching function centers.

[0056] Since the clustering object of the present invention is the color matching function, a distance function required for clustering needs to be set. In the distance function set in the present invention, the distance value between any two color matching functions is the average standard color difference of these two color matching functions in cross-media color reproduction. The average standard color difference is preferably recommended to use the average CIEDE2000 color difference, which is the 2000 standard color difference formula of the International Commission on Illumination (CIE). The method for calculating the average CIEDE2000 color difference belongs to the prior art, and a brief introduction is given below. Assume that there are two different color matching functions XYZ1 and XYZ2 currently, and the method for calculating the average CIEDE2000 color difference is as follows:

[0057] (a) Calculate the tristimulus values of 30 colors of the preferred memory color card PMCC under D65 illumination using XYZ1, and then calculate the trichromatic coordinates of the three primary colors of 4 different types of monitors using XYZ1, and reproduce the 30 colors of the preferred memory color card PMCC on the 4 monitors;

[0058] (b) Calculate the tristimulus values of 30 colors of the preferred memory color card PMCC under the D65 light source using XYZ2, and also calculate the tristimulus values of the 30 colors reproduced on the monitor in step (a) using XYZ2;

[0059] (c) Convert the two sets of tristimulus values obtained in (b) into CIELAB color coordinates. The white point used for conversion is the D65 light source calculated by XYZ2, and then calculate the CIEDE2000 color difference between the two sets of color coordinates in the CIELAB color coordinates. To ensure the reliability of the CIEDE2000 color difference, the CIEDE2000 color differences of 30 color stimuli under multiple monitors (preferably 4) are used as the distance metric between the two color matching functions XYZ1 and XYZ2.

[0060] In addition, other iterative processes of the k-means clustering method belong to the prior art and will not be elaborated here. The k-means clustering needs to be iterated until the clustering centers converge or the maximum number of iterations is reached. The maximum number of iterations set in the present invention is 1000 times. Finally, after the clustering algorithm iteration in step 102 above is completed, 10 classified observers can be determined from 200 observer color matching functions, including the fixed CIE 2015 2° color matching function and CIE 2015 10° color matching function.

[0061] In addition, in addition to obtaining the classified observer color matching functions corresponding to the clustering centers, the present invention also needs to obtain all user characteristic colors of the target user, and the characteristic color type is any one of memory color or Unique Hue.

[0062] The following briefly introduces the acquisition methods of memory color or Unique Hue.

[0063] A) Acquisition of memory color

[0064] When it comes to the sky, grass, and apples, people naturally think of blue, green, and red, which are memory colors. The colors of these familiar objects in daylight are stored in people's minds like a series of reference color samples, forming a relatively fixed reference standard. Memory colors are relatively independent and do not rely on external references. Each person's memory colors are relatively constant across different monitors and other media. However, due to differences in vision among different individuals, there are deviations between the memory colors of different individuals, which can be used to evaluate differences in individual color matching functions. Experimental methods for memory colors include matching experiments, forced-choice experiments, and other methods. There are two typical methods for matching experiments: Method 1 is to display a grayscale image containing memory color elements, such as a banana, and at the same time place a color patch. The observer adjusts the color of the color patch to match the color of the banana in memory. An example is shown in Figure 2 as shown; Method 2 is to directly adjust the color of the memory elements in this image to match the color of the banana in memory. An example is shown in Figure 3 as shown. The method for obtaining memory colors through forced-choice experiments is to render the colors of the images containing memory colors within a certain range, and the observer selects the image that conforms to the memory color to determine. An example is shown in Figure 4 as shown.

[0065] B) Obtaining characteristic hues

[0066] The concept of unique hue originated in the 1960s. It refers to the pure colors that people perceive in their minds without reference. Specifically, it includes unique red, unique yellow, unique green, and unique blue. The methods for obtaining characteristic hues mainly include selecting color samples closer to the characteristic colors in the mind without reference on a chromaticity circle with equal lightness and chroma, and adjusting the hue angle of color patches to match the pure colors in the mind. An example is shown in Figure 5 as shown.

[0067] It should be noted that in the process of obtaining the above characteristic colors, generally, a GUI interaction interface for characteristic color estimation experiments can be provided on the user's personal electronic device for the user to conduct the above memory color experiments or characteristic hue matching experiments and record the characteristic color estimation results. Among them, the matching experiment using object colors as references needs to be carried out in a CIE standard lighting environment, and the user's monitor is used to match the target color samples.

[0068] When all the classified observer color matching functions are obtained through step S1 and all the user characteristic colors of the target user are obtained through step S2, the personalized color matching function selection can be carried out. The specific method is to convert the spectral power distribution of each user characteristic color to the target color space based on each classified observer color matching function, and calculate the color difference between the converted user characteristic color coordinate value and the standard reference value of the user characteristic color. Since there is a series of different user characteristic colors, the average value of the color differences of all user characteristic colors can be calculated. Based on the screening principle of the smallest average value, the corresponding classified observer color matching function is screened out. This classified observer color matching function can replace the original color matching function in the display. This classified observer is the one close to the user's vision, thus realizing the personalized setting of color display.

[0069] It should be noted that the above target color space can be theoretically selected according to actual needs and can generally be set as the CIELAB space. Therefore, when converting the spectral power distribution of the user characteristic color to the target color space, it is necessary to calculate the respective tristimulus values of the spectral power of the user characteristic color and the white point based on each classified observer color matching function, and then convert the user characteristic color tristimulus value to the CIELAB space with the white point tristimulus value as the reference benchmark.

[0070] To better understand the process of selecting the personalized set color matching function above, the following takes the 10 classified observer color matching functions in the above embodiment as an example to illustrate the specific method of selecting the classified observer closest to the user's vision from 10 classified observers. This process is essentially to find a classified observer so that the characteristic color calculated by it has the smallest color difference from the average characteristic color. The specific method of the whole process is as follows:

[0071] (a) First, measure the spectral power distributions (SPDs) of the characteristic colors determined by the user's characteristic color estimation on the display device.

[0072] (b) Use the classified observer CMFs to calculate the corresponding XYZ tristimulus values and white point tristimulus values as follows:

[0073]

[0074] In the formula, K represents the luminous efficacy coefficient; represents the kth classified observer color matching function, whose range is from 390nm to 780nm in wavelength and is a matrix of size 3*391; represents the spectral power distribution of the user characteristic color estimation result, whose range is from 390nm to 780nm in wavelength and is a matrix of size 391*n; It represents the tristimulus values of the user's characteristic color calculated using the k-th class observer color matching function, which is a 3*n matrix; is the spectral power distribution corresponding to the D65 light source, which is a matrix of size 391*1, representing the CIE-standard illuminant D65; It represents the XYZ tristimulus values of the D65 light source calculated using this class observer color matching function, which can be used as the white point in subsequent calculations.

[0075] (c) The tristimulus values of the user's characteristic color calculated using the class observer color matching function in (b) are converted to Lab coordinates, and the white point used for the coordinate conversion is the XYZ tristimulus values of the D65 light source calculated using this class observer color matching function . Taking to represent the calculation function from XYZ to the CIELAB space, the above Lab coordinate conversion process can be expressed as:

[0076]

[0077] If the characteristic color is a memory color, the above Lab coordinates are calculated and that's it. But if the characteristic color is a characteristic hue, the Lab coordinates need to be further converted to LCh coordinates to obtain the hue angle of the k-th class observer , which is a 1*n matrix, and n is the number of characteristic hues (taking the value of 4).

[0078] (d) Find a class observer color matching function that can satisfy the condition that when calculating using this CMF, the difference between the user's own characteristic color and the characteristic color standard reference value is minimized.

[0079] Specifically, for the user memory color matching experiment, find a class observer CMF to minimize the average CIEDE2000 color difference between the user's own memory color and the memory color standard reference value. Where represents the CIEDE2000 color difference calculation formula, represents the average memory color difference calculated using the k-th class observer CMF. Finding the k-th class observer CMF to satisfy that it has the minimum color difference among all class observers, this optimization problem can be expressed as follows:

[0080]

[0081] is the reference color coordinate of the i-th memory color, which can be determined according to the PMCC color card; is The Lab coordinates corresponding to the i-th memory color in

[0082] In addition, for the user characteristic color tone experiment, find a classification observer CMF to minimize the root mean square error of the average color tone angle between the user's own characteristic color tone and the characteristic color tone standard reference value. Represents the root mean square error of the average color tone angle calculated by the k-th classification observer CMF. Represents finding the k-th classification observer CMF such that it can satisfy the minimum root mean square error of the average color tone angle among all classification observers. This optimization problem can be expressed as follows:

[0083]

[0084] In the formula: is the color tone angle of the i-th characteristic color tone in is the reference color tone angle of the i-th characteristic color tone, which can be determined according to the NCS average characteristic color tone.

[0085] The k-th classification observer CMF selected based on the above optimization problem is the color matching function closest to the user. Replacing the CIE standard color matching function used in the display device with this CMF can achieve a personalized color reproduction scheme.

[0086] It should be noted that the display personalized color display method described in S1~S3 above can actually be implemented in the form of computer programs and software function modules.

[0087] Therefore, based on the same inventive concept, the present invention provides a display personalized color display system, which includes:

[0088] A classification observer acquisition module for acquiring all classification observer color matching functions; the classification observer color matching functions are pre-clustered based on a data set composed of color matching functions of different observers. During the clustering process, several CIE standard color matching functions are set as fixed clustering centers, and the remaining clustering centers are iteratively optimized based on the distance values between color matching functions. The distance value between any two color matching functions is the average standard color difference of these two color matching functions in cross-media color reproduction. Each clustering center after iteration to convergence is used as a classification observer color matching function;

[0089] A characteristic color acquisition module for acquiring all user characteristic colors of the target user, where the characteristic color type is memory color or characteristic color tone;

[0090] The personalized color display module is used to convert the spectral power distribution of each user's characteristic color to the target color space based on each classification observer color matching function, and calculate the color difference between the converted user characteristic color coordinate value and the standard reference value of the user characteristic color; select the classification observer color matching function with the smallest average color difference of all user characteristic colors as the color matching function set by the display, and provide personalized color display for the target user.

[0091] Each module in the above display personalized color display system corresponds one-to-one with the display personalized color display method described in the foregoing S1~S3. Therefore, the specific practices in each module can also be referred to the foregoing embodiments.

[0092] In addition, based on the same inventive concept, as Figure 6 shown, the present invention also provides a computer electronic device corresponding to the display personalized color display method provided in the above embodiment, which includes a memory and a processor;

[0093] The memory is used to store computer programs;

[0094] The processor is used to implement the display personalized color display method as described above when executing the computer program;

[0095] In addition, when the logical instructions in the above memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention.

[0096] Therefore, based on the same inventive concept, the present invention provides a computer-readable storage medium corresponding to a display personalized color display method. A computer program is stored on the storage medium, and when the computer program is executed by a processor, it can implement the display personalized color display method as described above.

[0097] Therefore, based on the same inventive concept, the present invention provides a computer program product, including computer programs / instructions. When the computer programs / instructions are executed by a processor, they can implement the display personalized color display method as described above.

[0098] Specifically, in the computer-readable storage media of the above three embodiments, the stored computer program is executed by a processor, and the steps of S1 to S3 can be executed.

[0099] It can be understood that the above storage media may include a random access memory (RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory. At the same time, the storage media may also be various media such as a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc that can store program codes.

[0100] It can be understood that the above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0101] In addition, it should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described system can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here. In each of the embodiments provided in the present application, the division of steps or modules in the system and method is only a logical function division, and there may be other division methods in actual implementation. For example, multiple modules or steps can be combined or integrated together, and one module or step can also be split.

[0102] Next, the present invention will further show the detailed implementation process and technical effects of the display personalized color display method shown in the above S1 to S3 steps on a specific data set through a specific embodiment, so as to facilitate understanding the essence of the present invention.

[0103] Embodiment

[0104] The steps of this embodiment are the same as those of the display personalized color display method shown in the foregoing S1 to S3 steps, and will not be repeated here. The specific data set, some specific parameter settings, and implementation results of this embodiment will be mainly shown. The flow of the display personalized color display method in this embodiment is as Figure 7 shown.

[0105] 1. CMFs Data Acquisition

[0106] In this embodiment, the color matching functions of 100 individuals measured by the Zhejiang University team are used as a database, which is provided in the form of physiological visual parameters, including lens pigment optical density (Lens OD), macular pigment optical density (MPOD), L-cone photopigment optical density (L OD), M-cone photopigment optical density (M OD), S-cone photopigment optical density (S OD), L-cone spectrashifts (L shifts), and M-cone spectra shifts (M shifts). Using the Stockman-extended CIEPO06 model, the physiological visual parameters can be converted into continuous LMS color matching functions.

[0107] Using the Stockman-extended CIEPO06 model, the physiological visual parameters can be converted into continuous LMS color matching functions.

[0108]

[0109] A total of 200 color matching numbers for 2 degrees and 10 degrees are provided in the above table. The CIE 2015 2° color matching function and the CIE2015 10° color matching function are added to it, resulting in a total of 202 XYZ color matching functions. These 202 color matching functions are the color matching function dataset for classification.

[0110] 2. Observer Classification

[0111] Based on this dataset, it is divided into 10 categories using the K-means method. When using the clustering method, 10 cluster centers (centroids) are set. In each iteration, 8 out of the 10 cluster centers are randomly selected from 200 XYZ color matching functions, and the CIE 2015 2° color matching function and the CIE 2015 10° color are forced to be two fixed color matching function centers. When using the clustering method, the distance function is the average CIEDE2000 color difference in cross-media color reproduction of different XYZ color matching functions. In this embodiment, 10 classified observer color matching functions are finally obtained by clustering.

[0112] 3. Feature color estimation

[0113] In this embodiment, a user memory color experiment or a user feature color tone estimation experiment can be selected. The following takes the user memory color experiment as an example for elaboration.

[0114] In the user feature color tone estimation, a user conducted a feature color tone estimation experiment on a display. The selection was made using a color tone circle as shown in Figure 5 . The user selected feature red, feature yellow, feature green, and feature blue. Subsequently, the white light SPD of the display and the SPDs of the four feature colors of RYGB were measured.

[0115] The average feature color tone results using NCS color samples are as follows in the table:

[0116]

[0117] 4. Determine the user's visual classification

[0118] Using the 10 classified observer color matching functions obtained by clustering, the SPDs of the user's RYGB feature colors are sequentially converted to XYZ and then to LCh coordinates, and the root mean square error between the hue angles obtained by each classified observer color matching function and the average hue angle is calculated in turn. The root mean square errors of the average hue angles obtained by the 10 classified observers are as follows in the table.

[0119]

[0120] Based on this, it can be known that this user is closest to the 9th type of classified observer and has the smallest hue angle error. Using this color matching function to replace the CIE-standardized color matching function used in the display device can achieve a personalized color reproduction solution for this user.

[0121] The embodiments described above are only some preferred implementation solutions of the present invention, but are not intended to limit the present invention. Those of ordinary skill in the relevant technical field can still make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all technical solutions obtained by means of equivalent substitution or equivalent transformation fall within the protection scope of the present invention.

Claims

1. A method for personalized color display of a display, characterized in that, Including: S1. Obtain all classification observer color matching functions; the classification observer color matching functions are pre-clustered based on a dataset composed of color matching functions of different observers. During the clustering process, several CIE standard color matching functions are set as fixed clustering centers, and the remaining clustering centers are iteratively optimized based on the distance values between the color matching functions. The distance value between any two color matching functions is the average standard color difference of these two color matching functions in cross-media color reproduction. Each clustering center after iteration to convergence is used as a classification observer color matching function; S2. Obtain all user characteristic colors of the target user, where the characteristic color types are memory colors or characteristic color tones; S3. Based on each classification observer color matching function, convert the spectral power distribution of each user characteristic color to the target color space, and calculate the color difference between the converted user characteristic color coordinate values and the standard reference values of the user characteristic colors. Select the classification observer color matching function with the smallest average color difference among all user characteristic colors as the color matching function set for the display, and provide personalized color display for the target user.

2. The method for personalized color display of a display according to claim 1, characterized in that, The color matching functions in the dataset are XYZ color matching functions.

3. The method for personalized color display of a display according to claim 1, wherein The clustering uses k-means clustering, and the number of clustering centers is not less than 6.

4. The method for personalized color display of a display according to claim 1, characterized in that, During the clustering process, set the CIE 2015 2° color matching function and the CIE 2015 10° color matching function as two fixed clustering centers.

5. The method for personalized color display of a display according to claim 1, characterized in that, The average standard color difference uses the average CIEDE2000 color difference.

6. The method for personalized color display of a display according to claim 1, characterized in that The target color space is the CIELAB space. When converting the spectral power distribution of the user characteristic color to the target color space, it is necessary to calculate the tristimulus values corresponding to the spectral power of the user characteristic color and the white point respectively based on each classification observer color matching function, and then convert the user characteristic color tristimulus values to the CIELAB space with the white point tristimulus values as the reference benchmark.

7. The method for personalized color display of a display according to claim 1, characterized in that, The method for obtaining the user characteristic colors of the target user is: provide a GUI interaction interface for the characteristic color estimation experiment on the user's personal electronic device and record the characteristic color estimation results. The characteristic color estimation experiment is a memory color experiment or a characteristic color tone matching experiment.

8. A personalized color display system for a display, characterized in that, Including: A classification observer acquisition module for obtaining all classification observer color matching functions; the classification observer color matching functions are pre-clustered based on a dataset composed of color matching functions of different observers. During the clustering process, several CIE standard color matching functions are set as fixed clustering centers, and the remaining clustering centers are iteratively optimized based on the distance values between the color matching functions. The distance value between any two color matching functions is the average standard color difference of these two color matching functions in cross-media color reproduction. Each clustering center after iteration to convergence is used as a classification observer color matching function; A characteristic color acquisition module for obtaining all user characteristic colors of the target user, where the characteristic color types are memory colors or characteristic color tones; A personalized color display module is used to convert the spectral power distribution of each user's characteristic color to a target color space based on each classification observer color matching function, and calculate the color difference between the converted user characteristic color coordinate values and the standard reference values of the user characteristic colors; select the classification observer color matching function with the smallest average color difference among all user characteristic colors as the color matching function set by the display, and provide personalized color display for the target user.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the storage medium, and when the computer program is executed by a processor, the display personalized color display method according to any one of claims 1 to 7 is implemented.

10. A computer electronic device, characterized in that, It includes a memory and a processor; The memory is used to store a computer program; The processor is used to implement the display personalized color display method according to any one of claims 1 to 7 when executing the computer program.

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