Personalized color display method and system for display

Abstract

The invention discloses a personalized color display method and system for a display. The method aims at searching the most suitable color matching function of the classification observer in the database according to the estimated user characteristic color when the user uses the electronic equipment with the display screen, and the color matching function of the classification observer is used for replacing a CIE standard color matching function, so that personalized color reproduction is realized. According to the invention, different users can be ensured to have personalized color display on the display equipment, the purposes of accurate color display and personalization are achieved, and accurate color reproduction is provided for the 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 classified color vision observers. Background Art

[0002] Due to individual differences in macular and lens optical density, retinal photoreceptor pigment optical density, and spectral shifts in cone photoreceptor pigment spectra, the human color matching function (CMF) varies among different observers. These differences can 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 was largely ignored because most spectra of light in nature and traditional display devices are relatively broad, 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, resulting 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 viewing angles of 2° and 10°, for a total of 200 color matching functions. Existing research shows that although there are huge differences in human color matching functions among the population, 10 classified observers can 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 classified color matching functions are used to represent human 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 red and green hues without any traces of yellow and blue. Unique Yellow and Unique Blue are defined as 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, the factor that affects the unique color is the person's 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 classified 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 classified 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.

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

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

[0014] Preferably, as 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, as in the first aspect above, the average standard color difference uses the average CIEDE2000 color difference.

[0016] Preferably, as 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 classified 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, as 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. 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 classified observer acquisition module for acquiring 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 cluster centers, and the remaining cluster 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 cluster center after iteration to convergence is used as a classified 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 display personalized color display method described in any item of the first aspect above 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 implement the display personalized color display method described in any item of the first aspect above when executing the computer program.

[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 display personalized color display method and system, aiming to find the most suitable classification observer color matching function in a database according to the estimated user characteristic color when the 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 search optimization of 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 display personalized color display method of the present invention;

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

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

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

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

[0033] Figure 6 It is a schematic structural diagram of a computer electronic device;

[0034] Figure 7 It is a flowchart of an embodiment of the present invention. Detailed implementation manners

[0035] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention with reference to the accompanying drawings. Many specific details are set forth in the following description to fully understand 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 departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below. The technical features in various embodiments of the present invention can be combined correspondingly without conflict.

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

[0037] S1. Obtain 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, a number of 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.

[0038] S2. Obtain all user characteristic colors of the target user, where the type of characteristic color is a memory color or a feature 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 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 to provide personalized color display for the target user.

[0040] It should be noted that in the above S1 step, the acquisition method of all classification observer color matching functions needs to be understood in a broad sense. It can either be reading all the pre-clustered classification observer color matching functions from a data storage device or obtaining all the 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 data sources of the color matching function data in the data set include 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 follows: 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 obtained in Step 101

[0053] Taking the above-obtained 200 XYZ color matching functions 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, and 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 to the data set containing 200 XYZ color matching functions as fixed clustering centers, 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 the two fixed color matching function centers.

[0056] Since the clustering object of the present invention is the color matching function, it is necessary to set the distance function required for clustering. 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, 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 the 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 center converges 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, which include 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 the 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 retained in people's minds like a series of reference color samples, forming a relatively fixed reference standard. Memory colors are independent of external references and are relatively constant for each person on 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. The 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 make it 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 color that people perceive in their consciousness 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 consciousness 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 consciousness. 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 color as a reference needs to be carried out in a CIE standard illumination environment, and the user's monitor is used to match the target color sample.

[0068] After obtaining all the classification observer color matching functions through step S1 and all the user characteristic colors of the target user through step S2, personalized color matching function selection can be performed. The specific method is to 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 values and the standard reference values of the user characteristic colors. 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, and based on the screening principle of the smallest average value, the corresponding classification observer color matching function is screened out. This classification observer color matching function can replace the original color matching function in the display. This classification 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 selected according to actual needs in theory, and generally can be set as the CIELAB space. Thus, 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.

[0070] To better understand the process of selecting the personalized set color matching function above, taking the 10 classification observer color matching functions in the above embodiment as an example, the specific method of selecting the classification observer closest to the user's vision from the 10 classification observers is described below. This process is essentially to find a classification 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 classification observer CMFs to calculate the corresponding XYZ tristimulus values and white point tristimulus values, as shown in the following formula:

[0073]

[0074] In the formula, K represents the luminous efficacy coefficient; represents the k-th classification 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; denotes 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; denotes 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) Convert the tristimulus values of the user's characteristic color calculated using the class observer color matching function in (b) to Lab coordinates. The white point used for coordinate conversion is the XYZ tristimulus values of the D65 light source calculated using this class observer color matching function. . Let denote the calculation function for converting from XYZ to the CIELAB space. The above process of converting to Lab coordinates can be expressed as:

[0076]

[0077] If the characteristic color is a memory color, the above Lab coordinates can be calculated and that's it. However, 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 standard reference value of the characteristic color is minimized.

[0079] Specifically, for the user's 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 standard reference value of the memory color. Where denotes the CIEDE2000 color difference calculation formula, denotes the average memory color difference calculated using the k-th class observer CMF. Denotes finding the k-th class observer CMF to satisfy the condition 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 that 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 implement 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 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, so as to 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 function 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 essentially 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. This computer software product is stored in a storage medium and includes several instructions to enable 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 the 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 described above can be executed.

[0099] It can be understood that the above storage medium may include a random access memory (RAM), or may also include a non-volatile memory (NVM), such as at least one disk memory. At the same time, the storage medium 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 may be combined or integrated together, and a module or step may also be split.

[0102] Next, the present invention will further use a specific embodiment to demonstrate 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, 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 demonstrated. The process 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 team from Zhejiang University 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 hue estimation experiment can be selected. The following takes the user memory color experiment as an example for illustration.

[0114] In the user feature hue estimation, a user conducted a feature hue estimation experiment on a display. The selection was made using a hue 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 hue 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] Accordingly, 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 replacement or equivalent transformation fall within the protection scope of the present invention.

Claims

1. A method for displaying personalized colors, characterized in that: include: S1, obtaining all classified observer color matching functions; the classified observer color matching functions are obtained by clustering a data set composed of color matching functions of different observers in advance, and in the clustering process, a number of CIE standard color matching functions are set as fixed cluster centers, and the remaining cluster centers are iteratively optimized with the distance value between the color matching functions, and 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 iterative convergence is regarded as a classified observer color matching function; S2. Obtain all user characteristic colors of the target user, where the characteristic color type is a memory color or a characteristic hue; S3. Based on each classified observer color matching function, the spectral power distribution of each user characteristic color is converted to the target color space, and the color difference between the converted user characteristic color coordinate value and the standard reference value of the user characteristic color is calculated; the classified observer color matching function with the smallest average color difference of all user characteristic colors is selected as the color matching function set for the display, so as to provide personalized color display for the target user.

2. The display personalized color display method according to claim 1, characterized in that: The color matching function in the data set is an XYZ color matching function.

3. The display personalized color display method according to claim 1, characterized in that: The clustering adopts k-means clustering, and the number of cluster centers is not less than 6.

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

5. The display personalized color display method according to claim 1, characterized in that: The average standard color difference adopts the average CIEDE2000 color difference.

6. The display personalized color display method 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, the tristimulus values ​​corresponding to the spectral power of the user characteristic color and the white point need to be calculated respectively based on the color matching function of each classified observer, and then the tristimulus values ​​of the user characteristic color are converted to the CIELAB space with the white point tristimulus values ​​as a reference.

7. The display personalized color display method according to claim 1, characterized in that: The method for acquiring the user characteristic color of the target user is: providing a GUI interactive interface for performing a characteristic color estimation experiment on the user's personal electronic device and recording the characteristic color estimation result, wherein the characteristic color estimation experiment is a memory color experiment or a characteristic hue matching experiment.

8. A display personalized color display system, characterized in that: include: A classification observer acquisition module is used to acquire color matching functions of all classification observers; the classification observer color matching functions are obtained by clustering a data set composed of color matching functions of different observers in advance, and during the clustering process, a number of 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, and 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 iterative convergence is used as a classification observer color matching function; A characteristic color acquisition module is used to acquire all characteristic colors of the target user, wherein the characteristic color type is a memory color or a characteristic hue; The personalized color display module is used to convert the spectral power distribution of each user characteristic color into a 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; select the classified observer color matching function with the smallest average color difference of all user characteristic colors as the color matching function set for the display, so as to provide personalized color display for the target user.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the 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: including memory and processor; The memory is used to store computer programs; 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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