CN111324986A - Optimized color sample set obtaining method and device and color gamut index obtaining method and device - Google Patents
Optimized color sample set obtaining method and device and color gamut index obtaining method and device Download PDFInfo
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Abstract
The application provides an optimized color sample set obtaining method and device, a color gamut index obtaining method and device, and the optimized color sample set obtaining method comprises the following steps: acquiring color appearance parameters of various color samples in a large sample set; matching the lightness of each color sample with different lightness layers to obtain color samples of different lightness layers; matching the color samples of all lightness layers with different hue areas to obtain color samples positioned in the different hue areas; selecting a color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set; clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories; and taking the respective clustering centers of the color sample clustering sets corresponding to the color sample categories as representative color samples of the color sample clustering sets of the color sample categories to obtain an optimized color sample set.
Description
Technical Field
The application relates to the technical field of light source evaluation, in particular to an optimized color sample set obtaining method, an optimized color sample set obtaining device, a color gamut index obtaining method, a color gamut index obtaining device, an electronic device and a nonvolatile readable storage medium.
Background
The color gamut index of the light source can be used for evaluating the capability of the light source to realize higher color vividness of the illuminated object, and is a major hot spot for researching the color quality of the illumination light source in recent years. Some light sources, especially LED light sources with a sharp spectrum, are capable of accurately reproducing colors of low saturation, but perform poorly with color samples having high saturation. Therefore, the reproducibility of the light source for the color of a sample having high saturation needs to be evaluated. The light source color gamut index is used for representing the color reproducibility of a light source to a sample with high saturation, and is an important index for evaluating the color rendering property of the light source. In the existing method, when the light source color gamut index is used for evaluating the color rendering of the light source, the screening of color samples required by evaluation is mostly completed in a planar color gamut, so that the applicability of the screened color samples is limited, namely, the screened color samples have different losses when the saturation of the color samples with different lightness (namely, the color rendering of the light source) is evaluated, and the accuracy of the evaluation is influenced.
Disclosure of Invention
In view of the above, an object of the present application is to provide an optimized color sample set obtaining method, an optimized color sample obtaining device, a color gamut index obtaining method, a color gamut index obtaining device, an electronic device, and a non-volatile readable storage medium, which are used to improve applicability of the screened color samples, and further improve accuracy of light source color rendering evaluation by using the screened color samples.
The application provides an optimized color sample set obtaining method, which comprises the following steps: obtaining color appearance parameters of all color samples in a large sample set, wherein the color appearance parameters comprise brightness; matching the lightness of each color sample with the lightness ranges of different lightness layers to obtain color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals; matching the color sample of each lightness layer with the hue angle ranges of different hue areas to obtain the color samples positioned in the different hue areas, wherein the different hue areas are obtained by dividing hue angles at intervals according to equal hue angles; selecting a color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set; clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories; and taking the respective clustering centers of the color sample clustering sets corresponding to the color sample categories as representative color samples of the color sample clustering sets of the color sample categories to obtain an optimized color sample set.
According to the optimized color sample set obtaining method, the lightness of each color sample is matched with the lightness ranges of different lightness layers, so that the color samples of different lightness layers are obtained; matching the color samples of each lightness layer with the hue angle ranges of different hue areas to obtain the color samples in the different hue areas; selecting a color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set; clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories; and taking the respective clustering centers of the color sample cluster sets corresponding to the various color sample categories as representative color samples of the color sample cluster sets of the color sample categories to obtain an optimized color sample set, so that the finally obtained optimized color sample set can be suitable for evaluating the saturation (namely, the color rendering property of the light source) of the color samples with different lightness, and further the evaluation accuracy is improved to a certain extent.
Further, the acquiring color appearance parameters of the color samples in the initial color sample set includes: acquiring spectral data of all color samples in the large sample set; and obtaining color appearance parameters of the color samples in the large sample set through a color appearance model based on the spectral data of the color samples in the large sample set.
The large sample set can ensure the variety diversity of the color samples in the optimized color sample set finally screened, the optimized color sample set finally screened can be suitable for evaluating the saturation (namely, the color rendering property of the light source) of the color samples with different lightness to a certain extent, and the applicability of the optimized color sample set is improved.
Further, the clustering the color samples in the initial high saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories includes: performing dimensionality reduction processing on the spectral data of each color sample in the initial high-saturation color sample set to obtain main spectral data with the total contribution rate reaching a threshold value; fusing the main spectral data and the color appearance parameters to form fused data; and clustering the color samples in the initial high-saturation color sample set based on the fusion data to obtain a color sample cluster set of the multiple color sample categories.
The method comprises the steps of obtaining main spectral data of which the total contribution rate reaches a threshold value through dimension reduction processing on spectral data of all color samples in the initial high-saturation color sample set; fusing the main spectral data and the color appearance parameters to form fused data; clustering the color samples in the initial high-saturation color sample set based on the fusion data to obtain a color sample cluster set of multiple color sample categories, so that color appearance parameters and spectral data are considered during clustering, metamerism is avoided, the applicability of the finally obtained optimized color sample set is further improved, and the evaluation effect of the light source color rendering evaluation by using the finally obtained optimized color sample set is further improved; in addition, the calculation process can be simplified and the calculation amount can be reduced by reducing the dimension of the spectral data.
Further, the setting, as the representative color sample of the color sample cluster set of the color sample category, a cluster center of each color sample cluster set corresponding to each color sample category includes: respectively averaging the fusion data of all the color samples of all the color sample categories; and taking the color sample with the minimum sum of absolute errors with the mean value as a representative color sample.
In the application, the mean values of the fusion data of all the color samples of all the color sample categories are respectively calculated; the color sample with the minimum sum of absolute errors of the average values is used as a representative color sample, so that the problem that the clustering center cannot be directly obtained or the actually existing point in the color sample clustering set which does not correspond to the clustering center can be better solved.
The application also provides a color gamut index obtaining method, which comprises the following steps: obtaining color appearance parameters of all color samples in a large sample set, wherein the color appearance parameters comprise brightness; matching the lightness of each color sample with the lightness ranges of different lightness layers to obtain color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals; matching the color sample of each lightness layer with the hue angle ranges of different hue areas to obtain the color samples positioned in the different hue areas, wherein the different hue areas are obtained by dividing hue angles at intervals according to equal hue angles; selecting a color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set; clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories; taking the respective clustering centers of the color sample clustering sets corresponding to the color sample categories as representative color samples of the color sample clustering sets of the color sample categories to obtain an optimized color sample set; and obtaining the color gamut index of the light source according to the optimized color sample set.
According to the color gamut index obtaining method, the lightness of each color sample is matched with the lightness ranges of different lightness layers, so that the color samples of different lightness layers are obtained; matching the color samples of each lightness layer with the hue angle ranges of different hue areas to obtain the color samples in the different hue areas; selecting a color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set; clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories; and taking the respective clustering centers of the color sample cluster sets corresponding to the various color sample categories as representative color samples of the color sample cluster sets of the color sample categories to obtain an optimized color sample set, so that the finally obtained optimized color sample set can be suitable for evaluating the color sample saturation (namely, the color rendering property of the light source) with different lightness, and the evaluation accuracy can be improved to a certain extent by obtaining the color gamut index of the light source according to the optimized color sample set and then evaluating the color rendering property of the light source according to the obtained color gamut index, namely the evaluation accuracy is improved.
The application also provides an optimize look appearance collection obtaining device, includes: the acquisition module is used for acquiring color appearance parameters of all color samples in the large sample set, wherein the color appearance parameters comprise brightness; the matching module is used for matching the lightness of each color sample with the lightness range of different lightness layers to obtain the color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals, and the color samples of each lightness layer are matched with the hue angle range of different hue areas to obtain the color samples in different hue areas, wherein the different hue areas are obtained by dividing the hue angles at equal hue angle intervals; the selecting module is used for selecting the color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set; the clustering module is used for clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories; the selecting module is further configured to use the respective clustering centers of the color sample clustering sets corresponding to the color sample categories as representative color samples of the color sample clustering sets of the color sample categories to obtain an optimized color sample set.
Further, the obtaining module is configured to obtain spectral data of the various color samples in the large sample set, and obtain color appearance parameters of the various color samples in the large sample set through a color appearance model based on the spectral data of the various color samples in the large sample set.
Further, the clustering module is configured to perform dimensionality reduction on the spectral data of each color sample in the initial high-saturation color sample set to obtain main spectral data whose total contribution rate reaches a threshold, fuse the main spectral data and the color appearance parameters to form fused data, and cluster the color samples in the initial high-saturation color sample set based on the fused data to obtain a color sample cluster set of the multiple color sample categories.
Further, the selecting module is further configured to separately average the fused data of all color samples of each color sample category, and use the color sample with the smallest sum of absolute errors with the average as the representative color sample.
The present application further provides a color gamut index obtaining apparatus, including: the acquisition module is used for acquiring color appearance parameters of all color samples in the large sample set, wherein the color appearance parameters comprise brightness; the matching module is used for matching the lightness of each color sample with lightness ranges of different lightness layers to obtain color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals, and the color samples of each lightness layer are matched with hue angle ranges of different hue areas to obtain color samples in different hue areas, and the different hue areas are obtained by dividing the hue angles at equal hue angle intervals; the selecting module is used for selecting the color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set; the clustering module is used for clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories; the selecting module is further used for taking the respective clustering centers of the color sample clustering sets corresponding to the color sample categories as representative color samples of the color sample clustering sets of the color sample categories to obtain an optimized color sample set; and the calculation module is used for obtaining the light source color gamut index according to the optimized color sample set.
The present application further provides an electronic device, comprising a processor and a memory, where the memory stores computer-readable instructions, and the computer-readable instructions, when executed by the processor, cause the processor to execute the optimized color sample set obtaining method or the color gamut index obtaining method.
The present application also provides a non-transitory readable storage medium storing computer readable instructions that, when executed by a processor, cause the processor to perform the above-described optimized color sample set acquisition method or the above-described color gamut index acquisition method.
Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flow chart of an optimized color sample set obtaining method according to an embodiment of the present application.
Fig. 2 is a schematic flow chart of a color gamut index obtaining method according to an embodiment of the present application.
Fig. 3 is a block diagram of an optimized color sample set obtaining apparatus according to an embodiment of the present disclosure.
Fig. 4 is a block diagram of a color gamut index obtaining apparatus according to an embodiment of the present application.
Fig. 5 is a block diagram of an electronic device according to an embodiment of the present application.
Icon: optimizing the color sample set acquisition device 10; an acquisition module 11; a matching module 12; a selection module 13; a clustering module 14; a color gamut index acquisition means 20; a calculation module 15; an electronic device 100; a processor 101; a non-volatile storage medium 102; an internal memory 103; an input device 104; a display screen 105; a scanning device 106; a network interface 107; a system bus 108.
Detailed Description
The technical solution in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, an embodiment of the present application provides an optimized color sample set obtaining method. The method comprises the following steps.
Step S101, obtaining color appearance parameters of all color samples in a large sample set, wherein the color appearance parameters comprise brightness.
In this embodiment, the large sample set may include all or part of color samples in a color sample data set actually measured in nature, and/or include all or part of color sample spectral data in a color sample spectral data set artificially synthesized. Optionally, more than 10 thousand color samples are included in the large sample set. The large sample set can ensure the variety diversity of the color samples in the finally screened optimized color sample set, ensure that the finally screened optimized color sample set can be suitable for evaluating the saturation of the color samples with different lightness (namely, the color rendering of the light source) to a certain extent, and improve the applicability of the optimized color sample set.
In this embodiment, the color appearance parameters may include lightness J ', red-green degree a ', yellow-blue degree b ', and the like.
In the embodiment, the spectral data of all color samples in a large sample set can be acquired; and then, based on the spectral data of the various color samples in the large sample set, obtaining color appearance parameters of the various color samples in the large sample set through a color appearance model.
Specifically, the high-dimensional spectral reflectivity of each color sample in the large sample set is obtained, and then the high-dimensional spectral reflectivity of each color sample in the large sample set, the spectral data of the reference light source and the standard observer function (2-degree field of view, or 10-degree field of view, etc.) are input into the color appearance model to calculate the color appearance parameters of each color sample in the large sample set. It is understood that the calculation of color appearance parameters of color samples using a color appearance model is a known technique, and a detailed description of the specific process is not provided herein.
In this embodiment, the color appearance model may be a CAM16 color appearance model (embedded CAT16 color adaptation and CAM16-UCS color space). It is understood that in other embodiments, other color appearance models may be used as well, and the application is not limited thereto.
And step S102, matching the brightness of each color sample with the brightness range of different brightness layers to obtain the color samples of different brightness layers, wherein the different brightness layers are obtained by dividing the brightness at equal intervals.
In the present embodiment, the lightness (lightness) is divided into a plurality of different lightness layers at equal lightness intervals in advance. Each brightness layer corresponds to a different brightness range. The brightness interval according to which the division is based can be set as desired.
After the color appearance parameters of the color samples in the large sample set are obtained in step S101, the lightness of each color sample is matched with the lightness range of the different lightness layers, and when the lightness of a color sample is determined to be within the lightness range of a lightness layer, the color sample is considered as the color sample of the lightness layer, so as to obtain the color samples of the different lightness layers. It will be appreciated that the number of colour samples for each lightness layer may be different.
And step S103, matching the color samples of each lightness layer with the hue angle ranges of different hue areas to obtain the color samples positioned in the different hue areas, wherein the different hue areas are obtained by dividing the hue angles according to equal hue angle intervals.
In the present embodiment, after the lightness is divided into a plurality of different lightness layers at equal lightness intervals in advance, the hue (of the hue angle) of each lightness layer is divided into a plurality of different hue regions at equal hue intervals in advance. Each hue region for a different hue angle range. The selection of the hue angle interval on which the division is based can be set as desired.
After the color sample of each lightness layer is obtained in step S102, the hue angle of each color sample is matched with the hue angle range of a different hue region, and when the hue angle of the color sample is determined to be within the hue angle range of a hue region, the color sample is regarded as the color sample of the hue region, and the color sample of the different hue region is obtained. The hue angle of each color sample can be obtained by the formula hue angle h ═ arctan (yellow-blue degree b '/red-green degree a') based on the red-green degree a 'and yellow-blue degree b' in the color appearance parameters. It will be appreciated that the number of colour samples in each tone region may vary.
And step S104, selecting the color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set.
In this embodiment, the following formula may be used: the chroma C ═ (red-green chroma a '^ 2+ yellow-blue chroma b' ^2) ^0.5 selects the color sample with the maximum saturation in each tone area. The color samples with the highest saturation in each tone region of each lightness layer collectively form an initial high saturation color sample set.
And S105, clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories.
In this embodiment, clustering the color samples in the initial high saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories may be performed in the following manner.
Firstly, performing dimensionality reduction processing on spectral data of all color samples in an initial high-saturation color sample set to obtain main spectral data with the total contribution rate reaching a threshold value; then, fusing the main spectral data and the color appearance parameters to form fused data, wherein the fused data comprises multi-dimensional data formed by the main spectral data and the color appearance parameters; then, clustering is carried out on the color samples in the initial high-saturation color sample set based on the fusion data, and a color sample cluster set of a plurality of color sample categories is obtained. In this embodiment, spectral data of each color sample in the initial high-saturation color sample set is subjected to dimensionality reduction, so that a clustering calculation process can be simplified, the calculation amount is reduced, clustering is performed based on fusion data formed by fusion of main spectral data and color appearance parameters, the color appearance parameters and the spectral data can be considered during clustering, metamerism is avoided, the applicability of the finally obtained optimized color sample set is further improved, and the evaluation effect of the finally obtained optimized color sample set during light source color rendering evaluation is further improved. It can be understood that in other embodiments, the step of performing dimension reduction processing on the spectral data of each color sample in the initial high-saturation color sample set may also be omitted, and the spectral data of the color samples and the color appearance parameters are directly fused to form fused data.
In this embodiment, a principal component analysis spectrum dimensionality reduction method may be adopted to perform dimensionality reduction processing on the spectrum data of each color sample in the initial high-saturation color sample set, and since the dimensionality reduction method may be implemented by matlab codes, the method is a known technology, and a detailed description of a specific process thereof is not described here.
In the present embodiment, the main spectral data in which the total contribution rate reaches the threshold value is spectral data that is arranged in the front row after the dimension reduction processing and in which the total contribution rate reaches the threshold value. The threshold may be set as required, and in this embodiment, the threshold is 95%.
In this embodiment, the color samples in the initial high saturation color sample set are clustered based on the fusion data, and a K-means clustering algorithm, a hierarchical clustering algorithm, a neighbor propagation clustering algorithm, a self-organizing map neural network clustering algorithm, and other clustering algorithms can be selected as needed. When clustering, multi-dimensional data formed by three-dimensional color appearance parameters (lightness, red-green degree and yellow-blue degree) and main spectral data in the fusion data can be comprehensively considered for clustering. The specific process of clustering by using the clustering algorithm is a known technique, and the detailed description of the process is omitted here. It can be understood that, in other embodiments, different index numbers may be assigned to different color samples according to the lightness, the red-green degree, the yellow-blue degree, and the main spectrum data of the color samples, and the index numbers and the lightness, the red-green degree, the yellow-blue degree, and the main spectrum data of the color samples form corresponding relationships.
And step S106, taking the respective cluster centers of the color sample cluster sets corresponding to the color sample categories as representative color samples of the color sample cluster sets of the color sample categories to obtain an optimized color sample set.
In this embodiment, in order to better solve the problem that the clustering center cannot be directly obtained or the actual point in the color sample clustering set which does not correspond to the clustering center cannot be obtained, the clustering center of each color sample category may be used as the representative color sample in the following manner. Firstly, respectively averaging fused data of all color samples of all color sample categories; then, the color sample whose sum of absolute errors from the mean is the smallest is taken as the representative color sample.
Representative color samples of each of all color sample classes collectively form an optimized color sample set.
In the optimized color sample set obtaining method provided by this embodiment, the lightness of each color sample is matched with the lightness ranges of different lightness layers to obtain color samples of different lightness layers; matching the color samples of each lightness layer with the hue angle ranges of different hue areas to obtain the color samples in the different hue areas; selecting a color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set; clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories; and taking the respective clustering centers of the color sample cluster sets corresponding to the various color sample categories as representative color samples of the color sample cluster sets of the color sample categories to obtain an optimized color sample set, so that the finally obtained optimized color sample set can be suitable for evaluating the saturation (namely, the color rendering property of the light source) of the color samples with different lightness, and further the evaluation accuracy is improved to a certain extent.
Referring to fig. 2, an embodiment of the present application further provides a color gamut index obtaining method based on the same inventive concept. On the basis of the optimized color sample set acquisition method, after step S106, the color gamut index acquisition method further includes step S107: and obtaining the color gamut index of the light source according to the optimized color sample set. In this embodiment, the color gamut index of the light source is obtained through a color gamut index calculation formula according to the optimized color sample set. It can be understood that the specific process of obtaining the color gamut index of the light source by the color gamut index formula based on the color sample set is known in the art and will not be described herein again.
According to the color gamut index obtaining method, the lightness of each color sample is matched with the lightness ranges of different lightness layers, so that the color samples of different lightness layers are obtained; matching the color samples of each lightness layer with the hue angle ranges of different hue areas to obtain the color samples in the different hue areas; selecting a color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set; clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories; and taking the respective clustering centers of the color sample cluster sets corresponding to the various color sample categories as representative color samples of the color sample cluster sets of the color sample categories to obtain an optimized color sample set, so that the finally obtained optimized color sample set can be suitable for evaluating the color sample saturation (namely, the color rendering property of the light source) with different lightness, and the evaluation accuracy can be improved to a certain extent by obtaining the color gamut index of the light source according to the optimized color sample set and then evaluating the color rendering property of the light source according to the obtained color gamut index, namely the evaluation accuracy is improved.
Next, the technical effect of evaluating color rendering of a light source by using a color gamut index of the light source obtained by using an optimized color sample set will be described with reference to a specific example.
In this example, the large sample set includes actually measured color sample data existing in nature and artificially synthesized spectral data, and the number of color samples included in the large sample set is greater than 10 ten thousand. The standard observer of 10 degrees of the CIE1964 standard chromaticity system is used as a standard observer function, the light source D65 is used as a reference light source to calculate the CAM16-UCS color space, and the embedded CAT16 color adapts to the color appearance parameters (lightness J ', red-green degree a ', and yellow-blue degree b ') of each color sample in the large sample set. The lightness is divided at equal intervals of lightness interval of 1 to obtain a plurality of lightness layers, and the hue is divided at equal intervals of hue angle interval of 1 ° to obtain a plurality of hue regions. An initial high saturation set of color samples Ω g is obtained in the manner described in the foregoing method. The initial set of high saturation color samples Ω g contains 24843 high saturation color samples. Principal component spectrum dimensionality reduction is performed on 81-dimensional spectra of 24843 color samples (81-dimensional spectra are obtained by dividing light wavelengths of 380nm-780nm at equal intervals according to 5nm intervals), and top 3-dimensional main spectral data with contribution rates of 80.58%, 12.15% and 4.68% respectively and a total contribution rate of more than 97% is obtained. And fusing the main spectral data (three-dimensional data) and color appearance parameters (brightness, red-green degree and yellow-blue degree three-dimensional data) to obtain 6-dimensional fused data. And carrying out self-organizing mapping neural network clustering on the fusion data. The specific process of self-organizing mapping neural network clustering on the fusion data is roughly as follows:
1) initialization: each node (color sample) randomly initializes its own parameter (in this embodiment, the own parameter is data of each dimension in 6-dimensional fusion data formed by the color appearance parameter and the 3-dimensional main spectral data). The number of parameters of each node is the same as the input dimension.
2) For each input data, the node that most closely matches it is found. Assuming that the input is D-dimensional data, i.e., X ═ xi, i ═ 1, 2., D }, the most suitable node is found for the euclidean distance by the discriminant function:
wherein j represents the jth node, wjiData representing the ith dimension of the jth node.
3) After finding the active node I (x), updating the neighboring nodesAnd (4) nodes. Order SijRepresenting the distance between nodes i and j, to which update weights are assigned for nodes adjacent to i (x). Updating weights
Decreases with increasing distance between the node and the activation node i (x). The update weight can be expressed by the following equation.
Wherein the content of the first and second substances,
is I (x) the updated weight of the neighboring node, Sj,I(x)Represents the distance between the jth node and the i (x) th node,
where t is time, σ0Is a suitably chosen constant.
4) And updating parameters of the nodes according to a gradient descent method:
Δwji＝η(t).Tj，I(x)(t).(xi-wji)
wherein, Δ wjiIs the weight modifier, η (t) is the learning rate,
wherein t is time; x is the number ofiData representing an ith dimension; w is ajiData representing the ith dimension of the jth node.
Then, the iteration is carried out until convergence or the maximum number of iterations is reached.
Clustering the fused data through a self-organizing mapping neural network to finally obtain a color sample cluster set of 16 color sample categories, and respectively taking the respective clustering centers of the color sample cluster sets corresponding to the color sample categories as representative color samples of the color sample cluster set of the color sample categories to obtain an optimized color sample set Θ g containing 16 high-saturation optimized color samples.
In this example, when the color rendering of the light source is evaluated, the color gamut index Rg in the existing light source color gamut index is selected as the evaluation index. The method comprises the steps of taking an optimized color sample set Θ g containing 16 high-saturation optimized color samples, a color gamut index Rg in a CES color sample (IES-TM-30(IES system), using a current uniform color space CAM02-UCS and 99 standard color samples which are marked as CES color samples) and an original large sample set as test color samples, carrying out actual calculation on 1200 illumination light sources such as LEDs, fluorescent lamps and traditional light sources with different color temperatures, comparing differences between Rg obtained by calculation of the optimized color sample set Θ g and the CES color samples and Rg obtained by calculation of the original large sample set, and calculating an average absolute error MAD. As shown in table 1, the average absolute error MAD of the optimized color sample set Θ g is smaller and smaller than the MAD of the CES color sample, which indicates that the accuracy is higher when the color rendering of the light source is evaluated by obtaining the color gamut index of the light source through the optimized color sample set; and the color gamut index Rg value obtained by calculating the optimized color sample set Θ g is closer to the color gamut index Rg obtained by calculating the original large color sample set.
TABLE 1 mean absolute error of calculated Rg for g/CES and large sample set
Referring to fig. 3, based on the same inventive concept, an embodiment of the present application further provides an optimized color sample set obtaining apparatus 10, where the optimized color sample obtaining apparatus 10 includes an obtaining module 11, a matching module 12, a selecting module 13, and a clustering module 14.
The obtaining module 11 is configured to obtain color appearance parameters of each color sample in the large sample set, where the color appearance parameters include brightness. In an embodiment, the obtaining module 11 is configured to obtain spectral data of each color sample in the large sample set, and obtain color appearance parameters of each color sample in the large sample set through a color appearance model based on the spectral data of each color sample in the large sample set.
The matching module 12 is configured to match lightness of each color sample with lightness ranges of different lightness layers to obtain color samples of different lightness layers, where the different lightness layers are obtained by equally dividing the lightness, and the color samples of each lightness layer are matched with hue angle ranges of different hue areas to obtain color samples located in different hue areas, where the different hue areas are obtained by equally dividing the hue angles according to equal hue angles;
the selecting module 13 is configured to select a color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set.
The clustering module 14 is configured to cluster the color samples in the initial high saturation color sample set to obtain a color sample cluster set of multiple color sample categories. In an embodiment, the clustering module 14 is configured to perform dimension reduction on the spectral data of each color sample in the initial high-saturation color sample set to obtain main spectral data whose total contribution rate reaches a threshold, fuse the main spectral data and the color appearance parameters to form fused data, and cluster the color samples in the initial high-saturation color sample set based on the fused data to obtain a color sample cluster set of a plurality of color sample categories.
In this embodiment, the selecting module 13 is further configured to use the respective cluster centers of the color sample cluster sets corresponding to the color sample categories as representative color samples of the color sample cluster sets of the color sample categories to obtain the optimized color sample set. In an embodiment, the selecting module 13 is further configured to separately average the fused data of all the color samples of each color sample category, and use the color sample with the smallest sum of absolute errors from the average as the representative color sample.
It can be understood that the optimized color sample set obtaining device provided by the present application corresponds to the optimized color sample set obtaining method provided by the present application, and for brevity of the description, the same or similar parts may refer to the contents of the optimized color sample set obtaining method part, which is not described herein again.
Referring to fig. 4, based on the same inventive concept, an embodiment of the present application further provides a color gamut index obtaining device 20, where the color gamut index obtaining device 20 further includes a calculating module 15, on the basis of the optimized color sample set obtaining device 10, for obtaining the light source color gamut index according to the optimized color sample set. In an embodiment, the calculating module 15 is configured to obtain the color gamut index of the light source through a color gamut index calculation formula according to the optimized color sample set.
It can be understood that the color gamut index obtaining device 20 provided in the present application is based on the optimized color sample set obtaining device 10 and corresponds to the color gamut index obtaining method provided in the present application, and for brevity of the description, the same or similar parts may refer to the contents of the optimized color sample set obtaining device 10 and the color gamut index obtaining method part, and are not described herein again.
The respective modules in the optimized color sample set obtaining device and/or the color gamut index obtaining device may be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the server, and can also be stored in a memory in the server in a software form, so that the processor can call and execute operations corresponding to the modules. The processor can be a Central Processing Unit (CPU), a microprocessor, a singlechip and the like.
The above-mentioned optimized color sample set obtaining method and/or optimized color sample set obtaining device and/or color gamut index obtaining method and/or color gamut index obtaining device may be implemented in the form of computer readable instructions, which may be run on an electronic device as shown in fig. 5.
An electronic device according to an embodiment of the present application includes a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, where the processor implements the optimized color sample set obtaining method and/or the color gamut index obtaining method when executing the program.
Fig. 5 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, where the electronic device may be a mobile phone, a tablet computer, or the like. Referring to fig. 5, the electronic device 100 includes a processor 101, a nonvolatile storage medium 102, an internal memory 103, an input device 104, a display 105, a scanning device 106, and a network interface 107 connected via a system bus 108. The non-volatile storage medium of the electronic device 100 may store an operating system and computer readable instructions, and when the computer readable instructions are executed, the processor 101 may execute an optimized color sample set obtaining method and/or a color gamut index obtaining method according to embodiments of the present application, where a specific implementation process of the optimized color sample set obtaining method may refer to specific contents in fig. 1, and a specific implementation process of the color gamut index obtaining method may refer to specific contents in fig. 2, and details are not described herein again. The processor 101 of the electronic device 100 is used to provide computing and control capabilities to support the operation of the entire electronic device 100. The internal memory 103 may have stored therein computer-readable instructions that, when executed by the processor 101, may cause the processor 101 to perform an optimized color sample set acquisition method and/or a color gamut index acquisition method. The input device 104 of the electronic device 100 is used for inputting various parameters, the display screen 105 of the electronic device 100 is used for displaying, the scanning device 106 of the electronic device 100 is used for scanning graphic codes, and the network interface 107 of the electronic device 100 is used for network communication. Those skilled in the art will appreciate that the configuration shown in fig. 5 is a block diagram of only a portion of the configuration associated with the present application, and does not constitute a limitation on the electronic device to which the present application is applied, and a particular electronic device may include more or less components than those shown in the drawings, or may combine certain components, or have a different arrangement of components.
Based on the same inventive concept, embodiments of the present application provide a computer-readable storage medium, on which computer-readable instructions are stored, and the computer-readable instructions, when executed by a processor, implement the optimized color sample set obtaining method and/or the color gamut index obtaining method.
Any reference to memory, storage, database, or other medium used herein may include non-volatile. Suitable non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An optimized color sample set acquisition method is characterized by comprising the following steps:
obtaining color appearance parameters of all color samples in a large sample set, wherein the color appearance parameters comprise brightness;
matching the lightness of each color sample with the lightness ranges of different lightness layers to obtain color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals;
matching the color sample of each lightness layer with the hue angle ranges of different hue areas to obtain the color samples positioned in the different hue areas, wherein the different hue areas are obtained by dividing hue angles at intervals according to equal hue angles;
selecting a color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set;
clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories;
and taking the respective clustering centers of the color sample clustering sets corresponding to the color sample categories as representative color samples of the color sample clustering sets of the color sample categories to obtain an optimized color sample set.
2. The method for obtaining the optimized color sample set according to claim 1, wherein the obtaining of the color appearance parameters of each color sample in the initial color sample set comprises:
acquiring spectral data of all color samples in the large sample set;
and obtaining color appearance parameters of the color samples in the large sample set through a color appearance model based on the spectral data of the color samples in the large sample set.
3. The method according to claim 1, wherein the step of clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories comprises:
performing dimensionality reduction processing on the spectral data of each color sample in the initial high-saturation color sample set to obtain main spectral data with the total contribution rate reaching a threshold value;
fusing the main spectral data and the color appearance parameters to form fused data;
and clustering the color samples in the initial high-saturation color sample set based on the fusion data to obtain a color sample cluster set of the multiple color sample categories.
4. The method according to claim 1, wherein the step of using the respective cluster centers of the color sample cluster sets corresponding to the respective color sample categories as the representative color samples of the color sample cluster sets of the color sample categories comprises:
respectively averaging the fusion data of all the color samples of all the color sample categories;
and taking the color sample with the minimum sum of absolute errors with the mean value as a representative color sample.
5. A color gamut index acquisition method characterized by comprising:
obtaining color appearance parameters of all color samples in a large sample set, wherein the color appearance parameters comprise brightness;
matching the lightness of each color sample with the lightness ranges of different lightness layers to obtain color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals;
matching the color sample of each lightness layer with the hue angle ranges of different hue areas to obtain the color samples positioned in the different hue areas, wherein the different hue areas are obtained by dividing hue angles at intervals according to equal hue angles;
selecting a color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set;
clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories;
taking the respective clustering centers of the color sample clustering sets corresponding to the color sample categories as representative color samples of the color sample clustering sets of the color sample categories to obtain an optimized color sample set; and
and obtaining the color gamut index of the light source according to the optimized color sample set.
6. An optimized color sample set acquisition device, comprising:
the acquisition module is used for acquiring color appearance parameters of all color samples in the large sample set, wherein the color appearance parameters comprise brightness;
the matching module is used for matching the lightness of each color sample with the lightness range of different lightness layers to obtain the color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals, and the color samples of each lightness layer are matched with the hue angle range of different hue areas to obtain the color samples in different hue areas, wherein the different hue areas are obtained by dividing the hue angles at equal hue angle intervals;
the selecting module is used for selecting the color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set;
the clustering module is used for clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories;
the selecting module is further configured to use the respective clustering centers of the color sample clustering sets corresponding to the color sample categories as representative color samples of the color sample clustering sets of the color sample categories to obtain an optimized color sample set.
7. The device according to claim 6, wherein the clustering module is configured to perform dimensionality reduction on the spectral data of each color sample in the initial highly saturated color sample set to obtain main spectral data whose total contribution rate reaches a threshold, fuse the main spectral data and the color appearance parameters to form fused data, and cluster the color samples in the initial highly saturated color sample set based on the fused data to obtain the color sample cluster set of the plurality of color sample categories.
8. A color gamut index acquisition apparatus, characterized by comprising:
the acquisition module is used for acquiring color appearance parameters of all color samples in the large sample set, wherein the color appearance parameters comprise brightness;
the matching module is used for matching the lightness of each color sample with lightness ranges of different lightness layers to obtain color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals, and the color samples of each lightness layer are matched with hue angle ranges of different hue areas to obtain color samples in different hue areas, and the different hue areas are obtained by dividing the hue angles at equal hue angle intervals;
the selecting module is used for selecting the color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set;
the clustering module is used for clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories;
the selecting module is further used for taking the respective clustering centers of the color sample clustering sets corresponding to the color sample categories as representative color samples of the color sample clustering sets of the color sample categories to obtain an optimized color sample set; and
and the calculation module is used for obtaining the light source color gamut index according to the optimized color sample set.
9. An electronic device comprising a processor and a memory, the memory storing computer readable instructions which, when executed by the processor, cause the processor to perform the optimized color sample set acquisition method of any one of claims 1 to 4 or the color gamut index acquisition method of claim 5.
10. A non-transitory readable storage medium storing computer readable instructions that, when executed by a processor, cause the processor to perform the optimized color sample set acquisition method of any one of claims 1 to 4 or the color gamut index acquisition method of claim 5.
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