CN115547270B - Color difference adjustment method, device, equipment and storage medium based on spectrum analysis - Google Patents
Color difference adjustment method, device, equipment and storage medium based on spectrum analysis Download PDFInfo
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Abstract
The invention relates to a display color correction technology, and discloses a color difference adjustment method based on spectrum analysis, which comprises the following steps: acquiring an initial spectrum data set and a corresponding standard chromaticity value set of a RGB channel of a display screen; extracting color attribute spectrum characteristics of the initial spectrum data set, and selecting main color attribute spectrum characteristics from the color attribute spectrum characteristics by utilizing a main component analysis algorithm and an independent component analysis algorithm; selecting an optimal color matching function according to the spectrum radiation brightness, and carrying out weighting treatment on data with different dimensionalities in the main color attribute spectrum characteristics to obtain target spectrum characteristics; converting the target spectral characteristics into predicted color characteristic values by utilizing a pre-trained spectral mapping model; and calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and carrying out color calibration on the display screen according to the color difference. The invention further provides a chromatic aberration adjusting device based on spectrum analysis, electronic equipment and a storage medium. The invention can improve the color difference calibration accuracy of the display screen.
Description
Technical Field
The present invention relates to the field of display color correction technologies, and in particular, to a color difference adjustment method and apparatus based on spectral analysis, an electronic device, and a computer readable storage medium.
Background
With the development of new media technologies, LCD displays and the like are often used as a digital image display device for cross-media color reproduction. However, the problem of large chromatic aberration easily occurs during reproduction, which causes distortion of image colors, and thus color calibration of a display screen on a display device is required.
At present, color difference calibration is mainly carried out on a display screen from a chromaticity angle, an RGB color space is converted into a color space irrelevant to equipment through a mapping relation, then the color difference of the display screen is calibrated according to the difference value between the chromaticity value of the color space irrelevant to equipment and a standard color value, and the color difference calibration is not high in accuracy due to the fact that the color difference calibration is only considered from the color gamut, and the problem that different colors can be presented under different light sources easily occurs.
Disclosure of Invention
The invention provides a color difference adjustment method and device based on spectrum analysis and a computer readable storage medium, and mainly aims to solve the problem of low color difference calibration accuracy of a display screen.
In order to achieve the above object, the present invention provides a color difference adjustment method based on spectral analysis, including:
acquiring an initial spectrum data set and a corresponding standard chromaticity value set of an RGB channel of a display screen to be tested;
extracting color attribute spectrum characteristics of the initial spectrum data set, and selecting main color attribute spectrum characteristics from the color attribute spectrum characteristics by utilizing a main component analysis algorithm and an independent component analysis algorithm;
selecting an optimal color matching function according to the spectrum radiation brightness in the initial spectrum data set, and weighting data with different dimensions in the main color attribute spectrum characteristics by utilizing the optimal color matching function to obtain target spectrum characteristics;
converting the target spectral features into predicted color feature values by utilizing a pre-trained spectral mapping model;
and calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and carrying out color calibration on the display screen to be tested according to the color difference.
Optionally, the selecting the main color attribute spectral feature from the color attribute spectral features by using a principal component analysis algorithm and an independent component analysis algorithm includes:
sequencing the color attribute spectrum features according to the accumulated contribution rate by utilizing a principal component analysis algorithm, and selecting the color attribute spectrum features with the accumulated contribution rate larger than a preset threshold as initial main color attribute spectrum features;
Performing matrix transformation on tristimulus values in the initial spectrum data set to obtain the predicted spectrum reflectivity of the display screen to be tested;
calculating a spectral reflectance error value between the spectral reflectance in the initial spectral dataset and the predicted spectral reflectance;
analyzing the spectral reflectance error value by using the independent component analysis algorithm to obtain an updated spectral reflectance error value;
and reconstructing the initial main color attribute spectrum characteristic by using the updated spectrum reflection error value to obtain a main color attribute spectrum characteristic.
Optionally, the selecting the best color matching function according to the spectral radiance in the initial spectral dataset includes:
respectively weighting the spectrum radiation brightness in the initial spectrum data set by utilizing each color matching function in a preset color matching function set to obtain a weighted spectrum radiation brightness set;
respectively predicting a predicted RGB digital driving value corresponding to each weighted spectrum radiation brightness in the weighted spectrum radiation brightness set by using a preset spectrum characterization model;
acquiring a real RGB digital driving value corresponding to each weighted spectrum radiation brightness in the weighted spectrum radiation brightness set, and calculating a difference value between the predicted RGB digital driving value and the real RGB digital driving value;
And selecting a color matching function corresponding to the predicted RGB digital driving value with the minimum difference value as an optimal color matching function.
Optionally, the converting the target spectral feature into a predicted color feature value using a pre-trained spectral mapping model includes:
according to the weight of an input layer and the bias of an hidden layer in a pre-trained spectrum mapping model, weighting calculation is carried out on the target spectrum characteristic by utilizing an activation function of the hidden layer in the spectrum mapping model to obtain the output characteristic of each hidden layer;
and carrying out weighted calculation on the output characteristics of each hidden layer according to the weight of the output layer in the pre-trained spectrum mapping model to obtain a predicted color characteristic value.
Optionally, before the converting the target spectral feature into the predicted color feature value using the pre-trained spectral mapping model, the method further comprises:
acquiring a historical spectrum data set and a corresponding historical standard chromaticity value set of the RGB channel of the tested display screen;
extracting the historical main color attribute spectrum characteristics of the historical spectrum data set, selecting an optimal color matching function, and carrying out weighting treatment on data with different dimensionalities in the historical main color attribute spectrum characteristics to obtain historical target spectrum characteristics;
Randomly generating weights of input layers and bias of hidden layers in a plurality of pre-constructed spectrum mapping models to obtain a weight set of the input layers and a bias set of the hidden layers;
and respectively predicting the historical target spectral features by using the pre-constructed spectral mapping model according to the weight set and the bias set to obtain a predicted color feature value set, calculating an error value of the predicted color feature value set and a corresponding standard chromaticity value set, and taking the weight of an input layer and the bias of an hidden layer corresponding to the minimum error value as the weight of the input layer and the bias of the hidden layer in the pre-constructed spectral mapping model to obtain a pre-trained spectral mapping model.
Optionally, the extracting color attribute spectral features of the initial spectral dataset includes:
screening abnormal data in the spectrum data set by using a Markov distance algorithm, and removing noise in the spectrum data set by using a least square fitting method to obtain a first spectrum data set;
selecting spectral data of a band corresponding to the visible light color from the first spectral data set to obtain a second spectral data set;
and mapping the second spectrum data set into a spectrum curve, and extracting color attribute related characteristics in the spectrum curve as the color attribute spectrum characteristics of the display screen to be tested.
Optionally, the obtaining the initial spectrum data set and the corresponding standard chromaticity value set of the RGB channel of the display screen to be tested includes:
after the parameters of the display screen to be tested are stable, calibrating the display screen according to a preset display screen calibration rule;
generating a first color block set of the calibrated display screen to be tested by using a preset color block generation method according to a first preset interval value and an RGB digital driving value of a first preset condition, and collecting spectrum data sets of different wavelengths of a visible spectrum of the first color block set and corresponding standard chromaticity value sets;
generating a second color block set of the display screen to be tested after calibration by using the color block generation method according to a second preset interval value and an RGB digital driving value of a second preset condition, and collecting spectrum data sets of different wavelengths of a visible spectrum of the second color block set and corresponding standard chromaticity value sets;
and merging the spectrum data set of the first color block set and the spectrum data set of the second color block set to obtain an initial spectrum data set of the RGB channel of the display screen to be tested, and merging the standard chromaticity value set of the first color block set and the standard chromaticity value set of the second color block set to obtain a standard chromaticity value set corresponding to the RGB channel of the display screen to be tested.
In order to solve the above problems, the present invention further provides a color difference adjustment device based on spectral analysis, the device comprising:
the spectrum data acquisition module is used for acquiring an initial spectrum data set and a corresponding standard chromaticity value set of the RGB channel of the display screen to be tested;
the spectrum characteristic selecting module is used for extracting the color attribute spectrum characteristics of the initial spectrum data set and selecting main color attribute spectrum characteristics from the color attribute spectrum characteristics by utilizing a main component analysis algorithm and an independent component analysis algorithm;
the spectrum characteristic matching module is used for selecting an optimal color matching function according to the spectrum radiation brightness in the initial spectrum data set, and carrying out weighting processing on data with different dimensionalities in the main color attribute spectrum characteristic by utilizing the optimal color matching function to obtain a target spectrum characteristic;
the spectrum conversion module is used for converting the target spectrum characteristic into a predicted color characteristic value by utilizing a pre-trained spectrum mapping model;
and the color calibration module is used for calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and carrying out color calibration on the display screen to be tested according to the color difference.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the spectral analysis based color difference adjustment method described above.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned color difference adjustment method based on spectral analysis.
According to the embodiment of the invention, the initial spectrum data set and the corresponding standard chromaticity value set of the RGB channel of the display screen to be tested are obtained, the color attribute spectrum characteristics of the initial spectrum data set are extracted, the spectrum data quantity is reduced, so that the color difference correction efficiency is improved, the initial main color attribute spectrum characteristics are found out from the color attribute spectrum characteristics by using an independent component analysis algorithm, the initial main color attribute spectrum characteristics are reconstructed by using the independent component analysis algorithm, the main color attribute spectrum characteristics are obtained, the dimension-reduced spectrum characteristics are more accurate, and the color difference correction accuracy is improved; furthermore, an optimal color matching function is selected according to the spectrum radiation brightness in the initial spectrum data set, the data with different dimensions in the main color attribute spectrum characteristics are weighted to obtain target spectrum characteristics, the sensitivity of human eyes of an observer to the colors is fully considered, the variation of metamerism of the observer is reduced, and therefore the accuracy of color difference correction is improved; and finally, converting the target spectral characteristics into predicted color characteristic values by utilizing a pre-trained spectral mapping model, calculating the color difference between the predicted color characteristic values and the standard chromaticity value set, carrying out color calibration on the display screen to be tested according to the color difference, and converting spectral data into the predicted color characteristic values without being influenced by an ambient light source, thereby improving the color difference calibration accuracy of the display screen. Therefore, the color difference adjusting method, the device, the electronic equipment and the computer readable storage medium based on the spectrum analysis can solve the problem of low color difference calibration accuracy of the display screen.
Drawings
Fig. 1 is a flow chart of a color difference adjustment method based on spectral analysis according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a detailed implementation flow of one of the steps in the color difference adjustment method based on spectral analysis shown in FIG. 1;
FIG. 3 is a schematic diagram illustrating a detailed implementation flow of another step in the color difference adjustment method based on spectral analysis shown in FIG. 1;
FIG. 4 is a functional block diagram of a color difference adjusting device based on spectral analysis according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing the color difference adjustment method based on spectral analysis according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a chromatic aberration adjusting method based on spectrum analysis. The execution subject of the color difference adjustment method based on spectrum analysis includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the color difference adjustment method based on spectrum analysis may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a color difference adjustment method based on spectral analysis according to an embodiment of the invention is shown. In this embodiment, the color difference adjustment method based on spectral analysis includes:
s1, acquiring an initial spectrum data set and a corresponding standard chromaticity value set of an RGB channel of a display screen to be tested.
In the embodiment of the invention, the spectrum data set comprises reflection spectrum data, tristimulus values corresponding to spectrum radiation brightness and the like. Wherein the tristimulus values are representations of the amounts of stimulation of three primary colors causing the human retina to feel a certain color, expressed by X (red primary color stimulation amount), Y (green primary color stimulation amount) and Z (blue primary color stimulation amount).
Further, the chromaticity value is a color mode value, namely a Lab value, and includes three elements: brightness (L), and two color channels (a and b), wherein a includes colors from dark green (low brightness value) to gray (medium brightness value) to bright pink (high brightness value); b is from bright blue (low luminance value) to gray (medium luminance value) to yellow (high luminance value).
In the embodiment of the invention, any known device can be used to collect the initial spectrum data and the corresponding standard chromaticity value.
In detail, the acquiring the initial spectrum data set of the RGB channel of the display screen to be tested in S1 includes:
after the parameters of the display screen to be tested are stable, calibrating the display screen according to a preset display screen calibration rule;
generating a first color block set of the calibrated display screen to be tested by using a preset color block generation method according to a first preset interval value and an RGB digital driving value of a first preset condition, and collecting spectrum data sets of different wavelengths of a visible spectrum of the first color block set and corresponding standard chromaticity value sets;
generating a second color block set of the display screen to be tested after calibration by using the color block generation method according to a second preset interval value and an RGB digital driving value of a second preset condition, and collecting spectrum data sets of different wavelengths of a visible spectrum of the second color block set and corresponding standard chromaticity value sets;
and merging the spectrum data set of the first color block set and the spectrum data set of the second color block set to obtain an initial spectrum data set of the RGB channel of the display screen to be tested, and merging the standard chromaticity value set of the first color block set and the standard chromaticity value set of the second color block set to obtain a standard chromaticity value set corresponding to the RGB channel of the display screen to be tested.
In the embodiment of the invention, the display screen calibration rule is a rule that the color temperature, contrast and brightness of the display device are in standard values, so that the display device is prevented from being in an abnormal state.
The RGB digital driving value has a value range of (0, 255) and is expressed as three color gamuts of red (R), green (G) and blue (B).
Further, in the embodiment of the present invention, the first preset interval value is an RGB three-channel value respectively taking 8 as an interval, the RGB digital driving values of the first preset condition are (r=g, b=0) and (r=b, g=0), for example, when (r=g, b=0), the RGB digital driving value is 8, and the two-channel value data are (8,8,0), (16, 16,0) to (255, 0).
Further, in the embodiment of the present invention, the second preset interval value is that the RGB three channels take values at 15 intervals, and the RGB digital driving value of the second preset condition may be (r=g=b), (r++ 0,G =b=0), (g++0, r=b=0), (b++0, r=g=0), or (r=g=b=0).
Further, the preset color block generating method may be program code written by any software and capable of generating color blocks. In the embodiment of the invention, the wavelength range of the spectrum data set is 380 nm-780 nm, and the wavelength interval of different wavelengths is 1nm.
In another embodiment of the present invention, before the display screen to be tested is stable, the method may further include:
calibrating the display screen to be tested according to a preset display screen calibration rule, generating a device color characteristic file according to calibrated display screen parameters, and setting the device color characteristic file as a configuration file of the display screen system;
and testing the time stability, the space uniformity, the channel independence and the image stability of the display screen to be tested to obtain a test result, and returning to the step of calibrating the display screen to be tested according to a preset display screen calibration rule when the test result does not meet the preset standard, and calibrating the display screen to be tested again until the test result meets the preset standard.
In the embodiment of the invention, calibrating the display screen comprises calibrating the color temperature, contrast and brightness of the display screen.
In the embodiment of the invention, the device color Profile (ICC Profile) is used for describing the data set of the characteristics of a color input device, a color output device or a certain color space, and is formulated by the International Color Consortium (ICC) host, so that the color relation between the display RGB and the tristimulus value XYZ is recorded.
In the embodiment of the invention, the spectrum data of the color blocks corresponding to the RGB digital driving interval values are collected, so that the data sources are wider, and the accuracy of color difference correction is improved.
S2, extracting color attribute spectrum characteristics of the initial spectrum data set, and selecting main color attribute spectrum characteristics from the color attribute spectrum characteristics by utilizing a main component analysis algorithm and an independent component analysis algorithm.
In the embodiment of the present invention, the reflection spectrum data in the initial spectrum data generally includes several hundred-dimensional data, and it is difficult to directly process and analyze the spectrum data, and it is necessary to perform a dimension reduction process on the initial spectrum data.
In detail, the extracting the color attribute spectrum features of the initial spectrum dataset in S2 includes:
screening abnormal data in the spectrum data set by using a Markov distance algorithm, and removing noise in the spectrum data set by using a least square fitting method to obtain a first spectrum data set;
selecting spectral data of a band corresponding to the visible light color from the first spectral data set to obtain a second spectral data set;
and mapping the second spectrum data set into a spectrum curve, and extracting color attribute related characteristics in the spectrum curve as the color attribute spectrum characteristics of the display screen to be tested.
Because the perception of human eyes to colors is mainly determined by brightness, tone, chroma and the like, the brightness difference is reflected by the height of the spectrum curve, the tone of the colors is reflected by the wavelength corresponding to the peak value of the curve, the width of the peak of the curve represents the height of the chroma value of the colors, in the embodiment of the invention, the height of the spectrum curve and the width of the peak of the curve are directly related to the sum, the mean value, the maximum value and the peak reflectivity of the middle spectrum reflectivity in the spectrum curve, so that the sum, the mean value, the maximum value, the peak reflectivity and the wavelength corresponding to the peak value of the spectrum reflectivity in the spectrum curve of the display screen to be tested can be selected as the color attribute spectrum characteristics of the display screen to be tested.
In the embodiment of the invention, the color attribute spectrum characteristics of the initial spectrum data set are extracted, so that the spectrum data quantity can be reduced, and the prediction efficiency of the subsequent model can be improved.
Further, referring to fig. 2, the selecting, in S2, a principal color attribute spectral feature from the color attribute spectral features using a principal component analysis algorithm and an independent component analysis algorithm includes:
s21, sequencing the color attribute spectrum features according to the accumulated contribution rate by utilizing a principal component analysis algorithm, and selecting the color attribute spectrum features with the accumulated contribution rate larger than a preset threshold as initial main color attribute spectrum features;
S22, performing matrix transformation on tristimulus values in the initial spectrum data set to obtain the predicted spectrum reflectivity of the display screen to be tested;
s23, calculating a spectral reflectance error value between the spectral reflectance in the initial spectral dataset and the predicted spectral reflectance;
s24, analyzing the spectral reflection error value by using the independent component analysis algorithm to obtain an updated spectral reflection error value;
s25, reconstructing the initial main color attribute spectrum characteristic by using the updated spectrum reflection error value to obtain a main color attribute spectrum characteristic.
In the embodiment of the present invention, the principal component analysis algorithm is a statistical method, a set of variables that may have correlation is converted into a set of variables that are linearly uncorrelated through a forward-backward transformation, the converted variables are called principal components, and the independent component analysis algorithm (Independent component analysis, abbreviated as ICA) is a linear transformation, and data are separated into linear combinations of statistically independent non-gaussian signal sources.
In one embodiment of the present invention, the color attribute spectrum feature with the cumulative contribution rate greater than 85% may be selected as the initial primary color attribute spectrum feature.
In the embodiment of the invention, the principal component analysis algorithm can find the characteristic data with larger variance accumulation contribution rate as the principal component, and the residual spectrum error is easy to produce because the number of the basis functions of the spectrum reflectivity of the principal component analysis algorithm is limited, and the original main color attribute spectrum characteristic is further reconstructed by using the independent component analysis algorithm to obtain the main color attribute spectrum characteristic, so that the dimension-reduced spectrum characteristic is more accurate, and the accuracy of chromatic aberration correction is improved.
S3, selecting an optimal color matching function according to the spectrum radiation brightness in the initial spectrum data set, and carrying out weighting processing on data with different dimensions in the main color attribute spectrum characteristics by utilizing the optimal color matching function to obtain target spectrum characteristics.
The color matching function (Coloe Mathing Function, abbreviated as CMF) may be used to match the number of red, green and blue primary colors required for each primary color in the spectrum of the same energy. In the embodiment of the invention, the color matching function may include a CIE1931 standard observer matching function, a CIE1964 standard observer matching function, a spectral luminous efficiency function, an LMS cone response function, a personalized chromaticity model, and/or the like.
In detail, referring to fig. 3, the selecting the best color matching function according to the spectral radiance in the initial spectral dataset in S3 includes:
s31, respectively carrying out weighting treatment on the spectrum radiation brightness in the initial spectrum data set by utilizing each color matching function in a preset color matching function set to obtain a weighted spectrum radiation brightness set;
s32, respectively predicting a predicted RGB digital driving value corresponding to each weighted spectrum radiation brightness in the weighted spectrum radiation brightness set by using a preset spectrum characterization model;
s33, acquiring a real RGB digital driving value corresponding to each weighted spectrum radiation brightness in the weighted spectrum radiation brightness set, and calculating a difference value between the predicted RGB digital driving value and the real RGB digital driving value;
s34, selecting a color matching function corresponding to the predicted RGB digital driving value with the minimum difference value as an optimal color matching function.
In the embodiment of the present invention, the preset spectrum characterization model may be a spectrum radiance segmentation partition model (Spectral Radiance Piecewise Partition Model, abbreviated as SRPPM) or an LCD color characterization model (Solar Radiation Physical Modeling, abbreviated as SRPM) partitioned according to wavelength, where the spectrum characterization model establishes a correspondence between any RGB digital driving value and spectrum radiance.
In the embodiment of the invention, the data with different dimensionalities in the main color attribute spectrum characteristics are weighted by selecting the optimal color matching function to obtain the target spectrum characteristics, the sensitivity of human eyes of an observer to the color is fully considered, and the variation of metamerism of the observer is reduced, so that the accuracy of color difference correction is improved.
S4, converting the target spectral characteristics into predicted color characteristic values by utilizing a pre-trained spectral mapping model.
In detail, the S4 includes:
according to the weight of an input layer and the bias of an hidden layer in a pre-trained spectrum mapping model, weighting calculation is carried out on the target spectrum characteristic by utilizing an activation function of the hidden layer in the spectrum mapping model to obtain the output characteristic of each hidden layer;
and carrying out weighted calculation on the output characteristics of each hidden layer according to the weight of the output layer in the spectrum mapping model to obtain a predicted color characteristic value.
In the embodiment of the present invention, the pre-trained spectral mapping model is a model constructed by an extreme learning machine (Extreme Learning Machine, abbreviated as ELM), and can use spectral data as standard chromaticity values, where the spectral mapping model includes an input layer, an implicit layer, and an output layer, and an activation function of the implicit layer is a sigmoid function.
In one embodiment of the present invention, before S4, the method further includes:
acquiring a historical spectrum data set and a corresponding historical standard chromaticity value set of the RGB channel of the tested display screen;
extracting the historical main color attribute spectrum characteristics of the historical spectrum data set, selecting an optimal color matching function, and carrying out weighting treatment on data with different dimensionalities in the historical main color attribute spectrum characteristics to obtain historical target spectrum characteristics;
randomly generating weights of input layers and bias of hidden layers in a plurality of pre-constructed spectrum mapping models to obtain a weight set of the input layers and a bias set of the hidden layers;
and respectively predicting the historical target spectral features by using the pre-constructed spectral mapping model according to the weight set and the bias set to obtain a predicted color feature value set, calculating an error value of the predicted color feature value set and a corresponding standard chromaticity value set, and taking the weight of an input layer and the bias of an hidden layer corresponding to the minimum error value as the weight of the input layer and the bias of the hidden layer in the pre-constructed spectral mapping model to obtain a pre-trained spectral mapping model.
In the embodiment of the invention, the weight and the bias with the minimum error value are selected as the parameters of the spectrum mapping model, so that the spectrum mapping model is more accurate, the accuracy of the predicted color characteristic value is improved, and the accuracy of color difference correction is improved.
In another embodiment of the present invention, the parameters of the spectral mapping model may be optimized by using an ant colony algorithm, a genetic algorithm, or the like, so as to update the pre-constructed spectral mapping model.
In the embodiment of the invention, the target spectral characteristics are converted into the predicted color characteristic values by utilizing the pre-trained spectral mapping model, so that the color characteristic values are not influenced by an ambient light source, and the accuracy of the color characteristic values is improved.
S5, calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and carrying out color calibration on the display screen to be tested according to the color difference.
In the embodiment of the invention, the color difference between the predicted color characteristic value and the standard chromaticity value set can be calculated by using a CIEDE2000 color difference formula.
Further, in an embodiment of the present invention, the performing color calibration on the display screen to be tested according to the color difference includes:
performing color conversion processing on the color difference to obtain color adjustment data of the display screen to be tested;
and calibrating the color data of the display screen to be tested by utilizing the color adjustment data.
According to the embodiment of the invention, the initial spectrum data set and the corresponding standard chromaticity value set of the RGB channel of the display screen to be tested are obtained, the color attribute spectrum characteristics of the initial spectrum data set are extracted, the spectrum data quantity is reduced, so that the color difference correction efficiency is improved, the initial main color attribute spectrum characteristics are found out from the color attribute spectrum characteristics by using an independent component analysis algorithm, the initial main color attribute spectrum characteristics are reconstructed by using the independent component analysis algorithm, the main color attribute spectrum characteristics are obtained, the dimension-reduced spectrum characteristics are more accurate, and the color difference correction accuracy is improved; furthermore, an optimal color matching function is selected according to the spectrum radiation brightness in the initial spectrum data set, the data with different dimensions in the main color attribute spectrum characteristics are weighted to obtain target spectrum characteristics, the sensitivity of human eyes of an observer to the colors is fully considered, the variation of metamerism of the observer is reduced, and therefore the accuracy of color difference correction is improved; and finally, converting the target spectral characteristics into predicted color characteristic values by utilizing a pre-trained spectral mapping model, calculating the color difference between the predicted color characteristic values and the standard chromaticity value set, carrying out color calibration on the display screen to be tested according to the color difference, and converting spectral data into the predicted color characteristic values without being influenced by an ambient light source, thereby improving the color difference calibration accuracy of the display screen. Therefore, the color difference adjusting method based on spectrum analysis can solve the problem of low color difference calibration accuracy of the display screen.
Fig. 4 is a functional block diagram of a color difference adjusting device based on spectral analysis according to an embodiment of the present invention.
The color difference adjusting device 100 based on spectrum analysis according to the present invention may be installed in an electronic apparatus. Depending on the implementation, the color difference adjustment device 100 based on spectral analysis may include a spectral data acquisition module 101, a spectral feature selection module 102, a spectral feature matching module 103, a spectral conversion module 104, and a color calibration module 105. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the spectrum data acquisition module 101 is configured to acquire an initial spectrum data set and a corresponding standard chromaticity value set of an RGB channel of a display screen to be tested;
the spectral feature selection module 102 is configured to extract a color attribute spectral feature of the initial spectral dataset, and select a main color attribute spectral feature from the color attribute spectral features by using a main component analysis algorithm and an independent component analysis algorithm;
The spectral feature matching module 103 is configured to select an optimal color matching function according to the spectral radiance in the initial spectral dataset, and perform weighting processing on data with different dimensions in the main color attribute spectral feature by using the optimal color matching function to obtain a target spectral feature;
the spectrum conversion module 104 is configured to convert the target spectrum feature into a predicted color feature value by using a pre-trained spectrum mapping model;
the color calibration module 105 is configured to calculate a color difference between the predicted color feature value and the standard chromaticity value set, and perform color calibration on the display screen to be tested according to the color difference.
In detail, each module in the spectrum analysis-based color difference adjusting device 100 in the embodiment of the present invention adopts the same technical means as the above-mentioned spectrum analysis-based color difference adjusting method in fig. 1 to 3, and can produce the same technical effects, which are not described herein.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a chromatic aberration adjustment method based on spectral analysis according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a color difference adjustment program based on spectral analysis.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 11 (for example, executes a color difference adjustment program based on spectral analysis, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device and process data.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of a color difference adjustment program based on spectral analysis, but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 5 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The color difference adjustment program based on spectral analysis stored in the memory 11 in the electronic device 1 is a combination of instructions which, when run in the processor 10, can realize:
Acquiring an initial spectrum data set and a corresponding standard chromaticity value set of an RGB channel of a display screen to be tested;
extracting color attribute spectrum characteristics of the initial spectrum data set, and selecting main color attribute spectrum characteristics from the color attribute spectrum characteristics by utilizing a main component analysis algorithm and an independent component analysis algorithm;
selecting an optimal color matching function according to the spectrum radiation brightness in the initial spectrum data set, and weighting data with different dimensions in the main color attribute spectrum characteristics by utilizing the optimal color matching function to obtain target spectrum characteristics;
converting the target spectral features into predicted color feature values by utilizing a pre-trained spectral mapping model;
and calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and carrying out color calibration on the display screen to be tested according to the color difference.
In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
acquiring an initial spectrum data set and a corresponding standard chromaticity value set of an RGB channel of a display screen to be tested;
extracting color attribute spectrum characteristics of the initial spectrum data set, and selecting main color attribute spectrum characteristics from the color attribute spectrum characteristics by utilizing a main component analysis algorithm and an independent component analysis algorithm;
selecting an optimal color matching function according to the spectrum radiation brightness in the initial spectrum data set, and weighting data with different dimensions in the main color attribute spectrum characteristics by utilizing the optimal color matching function to obtain target spectrum characteristics;
converting the target spectral features into predicted color feature values by utilizing a pre-trained spectral mapping model;
and calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and carrying out color calibration on the display screen to be tested according to the color difference.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (6)
1. A color difference adjustment method based on spectral analysis, the method comprising:
acquiring an initial spectrum data set and a corresponding standard chromaticity value set of an RGB channel of a display screen to be tested;
extracting color attribute spectral features of the initial spectral data set, sorting the color attribute spectral features according to accumulated contribution rates by using a principal component analysis algorithm, selecting the color attribute spectral features with accumulated contribution rates larger than a preset threshold as initial main color attribute spectral features, performing matrix transformation on tristimulus values in the initial spectral data set to obtain predicted spectral reflectances of the display screen to be tested, calculating spectral reflection error values between the spectral reflectances in the initial spectral data set and the predicted spectral reflectances, analyzing the spectral reflection error values by using the independent component analysis algorithm to obtain updated spectral reflection error values, and reconstructing the initial main color attribute spectral features by using the updated spectral reflection error values to obtain main color attribute spectral features;
Respectively carrying out weighting treatment on the spectrum radiation brightness in the initial spectrum data set by utilizing each color matching function in a preset color matching function set to obtain a weighted spectrum radiation brightness set, respectively predicting a predicted RGB digital driving value corresponding to each weighted spectrum radiation brightness in the weighted spectrum radiation brightness set by utilizing a preset spectrum characterization model, obtaining a real RGB digital driving value corresponding to each weighted spectrum radiation brightness in the weighted spectrum radiation brightness set, calculating the difference value between the predicted RGB digital driving value and the real RGB digital driving value, selecting a color matching function corresponding to the predicted RGB digital driving value with the minimum difference value as an optimal color matching function, and carrying out weighting treatment on data with different dimensionalities in the spectrum characteristics of the main color attribute by utilizing the optimal color matching function to obtain a target spectrum characteristic;
acquiring a historical spectrum data set and a corresponding historical standard chromaticity value set of an RGB channel of a tested display screen, extracting historical main color attribute spectrum characteristics of the historical spectrum data set, selecting an optimal color matching function to perform weighted processing on data with different dimensionalities in the historical main color attribute spectrum characteristics to obtain historical target spectrum characteristics, randomly generating weights of input layers and bias of hidden layers in a plurality of pre-built spectrum mapping models to obtain a weight set of the input layers and a bias set of the hidden layers, respectively predicting the historical target spectrum characteristics by utilizing the pre-built spectrum mapping models according to the weight set and the bias set to obtain a predicted color characteristic value set, calculating error values of the predicted color characteristic value set and the corresponding standard chromaticity value set, and taking the weights of the input layers and bias of the hidden layers which are the smallest in the error values as the weights of the input layers and the bias of the hidden layers in the pre-built spectrum mapping models to obtain a pre-trained spectrum mapping model, wherein the pre-trained spectrum mapping model is constructed by a limit learning machine;
According to the weight of an input layer and the bias of an hidden layer in the spectrum mapping model, weighting calculation is carried out on the target spectrum characteristic by utilizing an activation function of the hidden layer in the spectrum mapping model to obtain an output characteristic of each hidden layer, and weighting calculation is carried out on the output characteristic of each hidden layer according to the weight of the output layer in the pre-trained spectrum mapping model to obtain a predicted color characteristic value;
and calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and carrying out color calibration on the display screen to be tested according to the color difference.
2. The spectral analysis based color difference adjustment method according to claim 1, wherein said extracting color attribute spectral features of the initial spectral dataset comprises:
screening abnormal data in the spectrum data set by using a Markov distance algorithm, and removing noise in the spectrum data set by using a least square fitting method to obtain a first spectrum data set;
selecting spectral data of a band corresponding to the visible light color from the first spectral data set to obtain a second spectral data set;
and mapping the second spectrum data set into a spectrum curve, and extracting color attribute related characteristics in the spectrum curve as the color attribute spectrum characteristics of the display screen to be tested.
3. The method for adjusting chromatic aberration based on spectral analysis according to claim 1, wherein the step of obtaining the initial spectral data set and the corresponding standard chromaticity value set of the RGB channel of the display screen to be tested includes:
after the parameters of the display screen to be tested are stable, calibrating the display screen according to a preset display screen calibration rule;
generating a first color block set of the calibrated display screen to be tested by using a preset color block generation method according to a first preset interval value and an RGB digital driving value of a first preset condition, and collecting spectrum data sets of different wavelengths of a visible spectrum of the first color block set and corresponding standard chromaticity value sets;
generating a second color block set of the display screen to be tested after calibration by using the color block generation method according to a second preset interval value and an RGB digital driving value of a second preset condition, and collecting spectrum data sets of different wavelengths of a visible spectrum of the second color block set and corresponding standard chromaticity value sets;
and merging the spectrum data set of the first color block set and the spectrum data set of the second color block set to obtain an initial spectrum data set of the RGB channel of the display screen to be tested, and merging the standard chromaticity value set of the first color block set and the standard chromaticity value set of the second color block set to obtain a standard chromaticity value set corresponding to the RGB channel of the display screen to be tested.
4. A color difference adjusting apparatus using the spectrum analysis-based color difference adjusting method of claim 1, characterized in that the apparatus comprises:
the spectrum data acquisition module is used for acquiring an initial spectrum data set and a corresponding standard chromaticity value set of the RGB channel of the display screen to be tested;
the spectrum characteristic selecting module is used for extracting the color attribute spectrum characteristics of the initial spectrum data set and selecting main color attribute spectrum characteristics from the color attribute spectrum characteristics by utilizing a main component analysis algorithm and an independent component analysis algorithm;
the spectrum characteristic matching module is used for selecting an optimal color matching function according to the spectrum radiation brightness in the initial spectrum data set, and carrying out weighting processing on data with different dimensionalities in the main color attribute spectrum characteristic by utilizing the optimal color matching function to obtain a target spectrum characteristic;
the spectrum conversion module is used for converting the target spectrum characteristic into a predicted color characteristic value by utilizing a pre-trained spectrum mapping model;
and the color calibration module is used for calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and carrying out color calibration on the display screen to be tested according to the color difference.
5. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the spectral analysis-based color difference adjustment method according to any one of claims 1 to 3.
6. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the spectral analysis-based color difference adjustment method according to any one of claims 1 to 3.
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