CN115547270A - Chromatic aberration adjusting method, device and equipment based on spectral analysis and storage medium - Google Patents
Chromatic aberration adjusting method, device and equipment based on spectral analysis and storage medium Download PDFInfo
- Publication number
- CN115547270A CN115547270A CN202211317947.6A CN202211317947A CN115547270A CN 115547270 A CN115547270 A CN 115547270A CN 202211317947 A CN202211317947 A CN 202211317947A CN 115547270 A CN115547270 A CN 115547270A
- Authority
- CN
- China
- Prior art keywords
- spectral
- color
- value
- data set
- display screen
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000010183 spectrum analysis Methods 0.000 title claims abstract description 41
- 230000004075 alteration Effects 0.000 title claims description 25
- 230000003595 spectral effect Effects 0.000 claims abstract description 294
- 238000001228 spectrum Methods 0.000 claims abstract description 76
- 238000013507 mapping Methods 0.000 claims abstract description 47
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 45
- 238000012880 independent component analysis Methods 0.000 claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 20
- 238000000513 principal component analysis Methods 0.000 claims abstract description 18
- 230000006870 function Effects 0.000 claims description 56
- 238000004590 computer program Methods 0.000 claims description 9
- 238000002310 reflectometry Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000012512 characterization method Methods 0.000 claims description 6
- 238000001429 visible spectrum Methods 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 230000009466 transformation Effects 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 230000004913 activation Effects 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims 1
- 238000012937 correction Methods 0.000 abstract description 12
- 238000005516 engineering process Methods 0.000 abstract description 4
- 239000003086 colorant Substances 0.000 description 8
- 238000004891 communication Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 238000007726 management method Methods 0.000 description 5
- 238000013473 artificial intelligence Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 230000035945 sensitivity Effects 0.000 description 3
- 230000001186 cumulative effect Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000005316 response function Methods 0.000 description 1
- 210000001525 retina Anatomy 0.000 description 1
- 230000035807 sensation Effects 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
- G09G3/00—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes
- G09G3/20—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters
- G09G3/34—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters by control of light from an independent source
- G09G3/36—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters by control of light from an independent source using liquid crystals
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
- G09G3/00—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes
- G09G3/20—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters
- G09G3/2003—Display of colours
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
- G09G2320/00—Control of display operating conditions
- G09G2320/06—Adjustment of display parameters
- G09G2320/0666—Adjustment of display parameters for control of colour parameters, e.g. colour temperature
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Crystallography & Structural Chemistry (AREA)
- Spectrometry And Color Measurement (AREA)
Abstract
The invention relates to a display color correction technology, and discloses a color difference adjusting method based on spectral analysis, which comprises the following steps: acquiring an initial spectrum data set and a corresponding standard chromaticity value set of a display screen RGB channel; extracting color attribute spectral features of the initial spectral data set, and selecting main color attribute spectral features from the color attribute spectral features by utilizing a principal component analysis algorithm and an independent component analysis algorithm; selecting an optimal color matching function according to the spectral radiance to perform weighting processing on data of different dimensions in the main color attribute spectral features to obtain target spectral features; converting the target spectral characteristics into predicted color characteristic values by using a pre-trained spectral mapping model; and calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and performing color calibration on the display screen according to the color difference. The invention also provides a color difference adjusting device based on spectral analysis, an electronic device and a storage medium. The invention can improve the accuracy of the display screen color difference calibration.
Description
Technical Field
The invention relates to the technical field of display color correction, in particular to a color difference adjusting method and device based on spectral analysis, electronic equipment and a computer readable storage medium.
Background
With the development of new media technologies, as a commonly used digital image display device, an LCD display and the like often perform color reproduction across media. However, the color of the image is distorted due to the problem that the color difference is large during the reproduction, so that the color calibration of the display screen on the display device is needed.
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, and then the color difference of the display screen is calibrated according to the chromaticity value of the color space irrelevant to the equipment and the difference value of a standard color value.
Disclosure of Invention
The invention provides a color difference adjusting method and device based on spectral analysis and a computer readable storage medium, and mainly aims to solve the problem of low accuracy of color difference calibration 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 spectral characteristics of the initial spectral data set, and selecting main color attribute spectral characteristics from the color attribute spectral characteristics by using a principal component analysis algorithm and an independent component analysis algorithm;
selecting an optimal color matching function according to the spectral radiance in the initial spectral data set, and performing weighting processing on data of different dimensions in the main color attribute spectral features by using the optimal color matching function to obtain target spectral features;
converting the target spectral feature into a predicted color feature value by using a pre-trained spectral mapping model;
and calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and performing color calibration on the display screen to be tested according to the color difference.
Optionally, the selecting, by using a principal component analysis algorithm and an independent component analysis algorithm, a principal color attribute spectral feature from the color attribute spectral features includes:
sorting the color attribute spectral features according to the accumulated contribution rate by utilizing a principal component analysis algorithm, and selecting the color attribute spectral features of which the accumulated contribution rate is greater than a preset threshold value as initial main color attribute spectral features;
performing matrix transformation on the 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 a spectral reflectance in the initial spectral data set and the predicted spectral reflectance;
analyzing the spectral reflection error value by using the independent component analysis algorithm to obtain an updated spectral reflection error value;
and reconstructing the initial main color attribute spectral characteristics by using the updated spectral reflection error value to obtain the main color attribute spectral characteristics.
Optionally, the selecting an optimal color matching function according to the spectral radiance in the initial spectral data set includes:
weighting the spectral radiance in the initial spectral data set by using each color matching function in a preset color matching function set to obtain a weighted spectral radiance set;
respectively predicting a prediction RGB digital driving value corresponding to each weighted spectral radiance in the weighted spectral radiance set by using a preset spectral characterization model;
acquiring a real RGB digital driving value corresponding to each weighted spectral radiance in the weighted spectral radiance set, and calculating a difference value between the predicted RGB digital driving value and the real RGB digital driving value;
and selecting the color matching function corresponding to the prediction RGB digital driving value with the minimum difference value as the optimal color matching function.
Optionally, the converting the target spectral feature into a predicted color feature value by using a pre-trained spectral mapping model includes:
according to the weight of an input layer in a pre-trained spectrum mapping model and the bias of a hidden layer, performing weighted calculation on the target spectrum characteristics by using an activation function of the hidden layer in the spectrum mapping model to obtain the output characteristics of each hidden layer;
and according to the weight of the output layer in the pre-trained spectral mapping model, performing weighted calculation on the output characteristic of each hidden layer to obtain a predicted color characteristic value.
Optionally, before converting the target spectral feature into a predicted color feature value using the pre-trained spectral mapping model, the method further includes:
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 spectral features of the historical spectral data set, selecting an optimal color matching function to perform weighting processing on data of different dimensions in the historical main color attribute spectral features to obtain historical target spectral features;
randomly generating weights of input layers and bias of hidden layers in a plurality of pre-constructed spectral mapping models to obtain a weight set of the input layers and a bias set of the hidden layers;
and predicting the historical target spectral features respectively 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 a 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 property spectral features of the initial spectral data set includes:
abnormal data in the spectral data set are screened out by using a Mahalanobis distance algorithm, and noise in the spectral data set is removed by using a least square fitting method to obtain a first spectral data set;
selecting spectral data of a wave band corresponding to visible light colors 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 color attribute spectrum characteristics of the display screen to be tested.
Optionally, the acquiring an initial spectrum data set of an RGB channel of a display screen to be tested and a corresponding standard chromaticity value set 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 a calibrated display screen to be tested by using a preset color block generation method according to a first preset interval value and RGB digital driving values of a first preset condition, and acquiring spectral data sets with 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 calibrated display screen to be tested by using the color block generating method according to a second preset interval value and RGB digital driving values of a second preset condition, and acquiring spectral data sets with different wavelengths of a visible spectrum of the second color block set and corresponding standard chromaticity value sets;
and combining the spectral data set of the first color block set and the spectral data set of the second color block set to obtain an initial spectral data set of the RGB channel of the display screen to be tested, and combining 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 problem, the present invention further provides a chromatic aberration adjusting apparatus based on spectral analysis, the apparatus comprising:
the spectrum data acquisition module is used for acquiring an initial spectrum data set and a corresponding standard chromaticity value set of an RGB channel of the display screen to be tested;
the spectral feature selection module is used for extracting color attribute spectral features of the initial spectral data set and selecting main color attribute spectral features from the color attribute spectral features by utilizing a principal component analysis algorithm and an independent component analysis algorithm;
the spectral feature matching module is used for selecting an optimal color matching function according to the spectral radiance in the initial spectral data set, and performing weighting processing on data with different dimensions in the main color attribute spectral features by using the optimal color matching function to obtain target spectral features;
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 problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
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 problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, the at least one computer program being executed by a processor in an electronic device to implement the spectral analysis-based color difference adjustment method described above.
According to the embodiment of the invention, the color attribute spectral characteristics of the initial spectral data set are extracted by acquiring the initial spectral data set and the corresponding standard chromaticity value set of the RGB channel of the display screen to be tested, the number of spectral data is reduced, so that the efficiency of chromatic aberration correction is improved, the initial main color attribute spectral characteristics are further found from the color attribute spectral characteristics by using an independent component analysis algorithm, the initial main color attribute spectral characteristics are reconstructed by using the independent component analysis algorithm, the main color attribute spectral characteristics are obtained, the dimension reduction spectral characteristics are more accurate, and the accuracy of chromatic aberration correction is improved; furthermore, an optimal color matching function is selected according to the spectral radiance in the initial spectral data set, weighting processing is carried out on data with different dimensionalities in the main color attribute spectral features, target spectral features are obtained, the sensitivity of human eyes of an observer to colors is fully considered, the difference of metamerism of the observer is reduced, and therefore the accuracy of chromatic aberration correction is improved; and finally, converting the target spectral feature into a predicted color feature value by using a pre-trained spectral mapping model, calculating the color difference between the predicted color feature value and the standard chromaticity value set, performing color calibration on the display screen to be tested according to the color difference, converting spectral data into the predicted color feature value, and avoiding the influence of an environmental light source, thereby improving the accuracy of the color difference calibration of the display screen. Therefore, the chromatic aberration adjusting method, the chromatic aberration adjusting device, the electronic equipment and the computer-readable storage medium based on spectral analysis can solve the problem of low chromatic aberration calibration accuracy of the display screen.
Drawings
Fig. 1 is a schematic flowchart of a chromatic aberration adjustment method based on spectral analysis according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a detailed implementation of one step in the chromatic aberration adjustment method based on spectral analysis shown in FIG. 1;
FIG. 3 is a schematic diagram illustrating another step of the method for adjusting chromatic aberration based on spectral analysis shown in FIG. 1;
FIG. 4 is a functional block diagram of a chromatic aberration adjustment apparatus 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 implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a color difference adjusting method based on spectral analysis. The execution subject of the color difference adjustment method based on spectral analysis includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the color difference adjustment method based on spectral analysis may be performed by software or hardware installed in a terminal device or a server device, and the software may be a block chain platform. The server 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 basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart of a chromatic aberration adjustment method based on spectral analysis according to an embodiment of the present invention. 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. The tristimulus values are expressed by the amounts of stimulus of three primary colors causing a certain color sensation to the retina of the human body, and are expressed by X (red primary color stimulus amount), Y (green primary color stimulus amount), and Z (blue primary color stimulus amount).
Further, the colorimetric value is a color model value, i.e. Lab value, and includes three elements: luminance (L), and two color channels (a and b), where a includes colors from dark green (low luminance value) to gray (medium luminance value) to bright pink red (high luminance value); b is from bright blue (low brightness value) to gray (medium brightness value) to yellow (high brightness value).
In the embodiment of the present invention, any known device may be used to collect the initial spectral data and the corresponding standard colorimetric values.
In detail, the acquiring of 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 a calibrated display screen to be tested by using a preset color block generation method according to a first preset interval value and RGB digital driving values of a first preset condition, and acquiring spectral data sets with 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 calibrated display screen to be tested by using the color block generating method according to a second preset interval value and RGB digital driving values of a second preset condition, and acquiring spectral data sets with different wavelengths of a visible spectrum of the second color block set and corresponding standard chromaticity value sets;
and combining the spectral data set of the first color block set and the spectral data set of the second color block set to obtain an initial spectral data set of the RGB channel of the display screen to be tested, and combining 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, the contrast and the brightness of the display equipment are in standard values, so that the display equipment is prevented from being in an abnormal state.
The value range of the RGB digital driving value is (0, 255), and the RGB digital driving value is represented by 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 that the RGB triple channels take values respectively at intervals of 8, and 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 data taken by the two channels are (8,8,0), (16, 16,0) - (255, 255,0) starting from the RGB digital driving value of 8.
Further, in this embodiment of the present invention, the second preset interval value is obtained at 15 intervals for the RGB three channels, and the RGB digital driving values 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 a program code written by any software that can generate 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 an equipment color characteristic file according to the calibrated display screen parameters, and setting the equipment color characteristic file as a configuration file of the display screen system;
and when the test result does not meet the preset standard, returning to the step of calibrating the display screen to be tested according to the preset display screen calibration rule to calibrate the display screen to be tested again until the test result meets the preset standard.
In the embodiment of the invention, the calibration of the display screen comprises the calibration of the color temperature, the contrast and the brightness of the display screen.
The device color Profile (ICC Profile) in the embodiment of the present invention is a data set for describing characteristics of a color input/output device or a certain color space, and is specified by International Color Consortium (ICC), and records a color relationship between display RGB and tristimulus values XYZ.
In the embodiment of the invention, the spectrum data of the color blocks corresponding to the multiple RGB digital driving interval values are collected, so that the data source is wider, and the accuracy of chromatic aberration correction is improved.
S2, extracting color attribute spectral features of the initial spectral data set, and selecting main color attribute spectral features from the color attribute spectral features by utilizing a principal 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 dimension reduction processing needs to be performed on the initial spectrum data.
In detail, the extracting color attribute spectral features of the initial spectral data set in S2 includes:
abnormal data in the spectral data set are screened out by using a Mahalanobis distance algorithm, and noise in the spectral data set is removed by using a least square fitting method to obtain a first spectral data set;
selecting spectral data of a wave band corresponding to visible light colors 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 color attribute spectrum characteristics of the display screen to be tested.
In the embodiment of the invention, the height of the spectral curve and the width of the curve peak are directly related to the sum, the mean value, the maximum value and the peak reflectivity of the spectral reflectance in the spectral curve, so that the sum, the mean value, the maximum value, the peak reflectivity and the wavelength corresponding to the peak value in the spectral curve of the display screen to be tested can be selected as the color attribute spectral characteristic of the display screen to be tested.
In the embodiment of the invention, the color attribute spectral characteristics of the initial spectral data set are extracted, so that the number of spectral data can be reduced, and the prediction efficiency of a subsequent model is improved.
Further, referring to fig. 2, the selecting, by using a principal component analysis algorithm and an independent component analysis algorithm, a principal color attribute spectral feature from the color attribute spectral features in S2 includes:
s21, sorting the color attribute spectral features according to accumulated contribution rates by utilizing a principal component analysis algorithm, and selecting the color attribute spectral features of which the accumulated contribution rates are larger than a preset threshold value as initial main color attribute spectral features;
s22, carrying out matrix transformation on the 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 reflection error value between the spectral reflectivity in the initial spectral data set and the predicted spectral reflectivity;
s24, analyzing the spectral reflection error value by using the independent component analysis algorithm to obtain an updated spectral reflection error value;
and S25, reconstructing the initial main color attribute spectral characteristics by using the updated spectral reflection error value to obtain the main color attribute spectral characteristics.
In the embodiment of the present invention, the principal component analysis algorithm is a statistical method, a group of variables that may have correlation are converted into a group of linearly uncorrelated variables through orthogonal transformation, the converted variables are called principal components, and the Independent component analysis algorithm (ICA) is a linear transformation, and data is separated into linear combinations of statistically Independent non-gaussian signal sources.
In an embodiment of the present invention, the color attribute spectral feature with the cumulative contribution rate greater than 85% may be selected as the initial main color attribute spectral feature.
In the embodiment of the invention, the principal component analysis algorithm can find the larger characteristic data in the variance cumulative contribution rate as the principal component, the number of basis functions of the spectral reflectivity of the principal component analysis algorithm is limited, so that the residual spectral error is easily caused, the initial main color attribute spectral characteristic is reconstructed by further utilizing the independent component analysis algorithm to obtain the main color attribute spectral characteristic, so that the dimension-reduced spectral characteristic is more accurate, and the accuracy of chromatic aberration correction is improved.
And S3, selecting an optimal color matching function according to the spectral radiance in the initial spectral data set, and performing weighting processing on data with different dimensions in the main color attribute spectral characteristics by using the optimal color matching function to obtain target spectral characteristics.
The Color Matching Function (CMF) can be used for matching the number of red, green and blue light with three primary colors required by each monochromatic light in the spectrum with equal energy. In an embodiment of the present 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, and/or a personalized chromaticity model.
In detail, referring to fig. 3, the selecting an optimal color matching function according to the spectral radiance in the initial spectral data set in S3 includes:
s31, weighting the spectral radiance in the initial spectral data set by using each color matching function in a preset color matching function set respectively to obtain a weighted spectral radiance set;
s32, respectively predicting a prediction RGB digital driving value corresponding to each weighted spectral radiance in the weighted spectral radiance set by using a preset spectral characterization model;
s33, acquiring a real RGB digital driving value corresponding to each weighted spectral radiance in the weighted spectral radiance set, and calculating a difference value between the predicted RGB digital driving value and the real RGB digital driving value;
and S34, selecting the color matching function corresponding to the prediction RGB digital driving value with the minimum difference value as the optimal color matching function.
In the embodiment of the present invention, the preset Spectral characterization Model may be a Spectral Radiance Piecewise Partitioning Model (SRPPM) or a wavelength-partitioned LCD color characterization Model (SRPM), and the Spectral characterization Model establishes a corresponding relationship between any RGB digital driving value and Spectral Radiance.
In the embodiment of the invention, the optimal color matching function is selected to perform weighting processing on the data with different dimensions in the main color attribute spectral features to obtain the target spectral features, the sensitivity of human eyes of an observer to colors is fully considered, and the difference of metamerism of the observer is reduced, so that the accuracy of chromatic aberration correction is improved.
And 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 in a pre-trained spectrum mapping model and the bias of a hidden layer, performing weighted calculation on the target spectrum characteristics by using an activation function of the hidden layer in the spectrum mapping model to obtain the output characteristics of each hidden layer;
and according to the weight of the output layer in the spectrum mapping model, carrying out weighted calculation on the output characteristic of each hidden layer to obtain a predicted color characteristic value.
In the embodiment of the invention, the pre-trained spectrum mapping model is a model constructed by an Extreme Learning Machine (ELM for short), spectrum data can be used as a standard colorimetric value, the spectrum mapping model comprises an input layer, a hidden layer and an output layer, and an activation function of the hidden layer is a sigmoid function.
In an 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 an RGB channel of a tested display screen;
extracting historical main color attribute spectral features of the historical spectral data set, selecting an optimal color matching function to perform weighting processing on data of different dimensions in the historical main color attribute spectral features to obtain historical target spectral features;
randomly generating weights of input layers and bias of hidden layers in a plurality of pre-constructed spectral mapping models to obtain a weight set of the input layers and a bias set of the hidden layers;
and predicting the historical target spectral features respectively 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 a 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 offset 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 predicting the 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 spectrum mapping model may be optimized by using an ant colony algorithm, a genetic algorithm, and the like, so as to update the pre-constructed spectrum mapping model.
In the embodiment of the invention, the target spectrum characteristic is converted into the predicted color characteristic value by utilizing the pre-trained spectrum mapping model without being influenced by the environment light source, so that the accuracy of the color characteristic value is improved.
And S5, calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and performing color calibration on the display screen to be tested according to the color difference.
In the embodiment of the present invention, the color difference between the predicted color characteristic value and the standard chromaticity value set may be calculated by using a CIEDE2000 color difference formula.
Further, in the embodiment of the present invention, the color calibration of the display screen to be tested according to the color difference includes:
carrying out 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 using the color adjusting data.
According to the embodiment of the invention, the color attribute spectral characteristics of the initial spectral data set are extracted by acquiring the initial spectral data set and the corresponding standard chromaticity value set of the RGB channel of the display screen to be tested, the number of spectral data is reduced, so that the efficiency of chromatic aberration correction is improved, the initial main color attribute spectral characteristics are further found from the color attribute spectral characteristics by using an independent component analysis algorithm, the initial main color attribute spectral characteristics are reconstructed by using the independent component analysis algorithm, the main color attribute spectral characteristics are obtained, the dimension reduction spectral characteristics are more accurate, and the accuracy of chromatic aberration correction is improved; furthermore, an optimal color matching function is selected according to the spectral radiance in the initial spectral data set, weighting processing is carried out on data with different dimensions in the spectral features of the main color attributes to obtain target spectral features, the sensitivity of human eyes of an observer to colors is fully considered, the difference of metamerism of the observer is reduced, and therefore the accuracy of color difference correction is improved; and finally, converting the target spectral feature into a predicted color feature value by using a pre-trained spectral mapping model, calculating the color difference between the predicted color feature value and the standard chromaticity value set, performing color calibration on the display screen to be tested according to the color difference, converting spectral data into the predicted color feature value, and avoiding the influence of an environmental light source, thereby improving the accuracy of the color difference calibration of the display screen. Therefore, the chromatic aberration adjusting method based on spectral analysis can solve the problem of low chromatic aberration calibration accuracy of the display screen.
Fig. 4 is a functional block diagram of a chromatic aberration adjustment apparatus based on spectral analysis according to an embodiment of the present invention.
The chromatic aberration adjusting apparatus 100 based on spectral analysis according to the present invention can be installed in an electronic device. According to the realized functions, the color difference adjusting apparatus 100 based on spectral analysis may include a spectral data obtaining module 101, a spectral feature selecting module 102, a spectral feature matching module 103, a spectral conversion module 104, and a color calibration module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding 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 color attribute spectral features of the initial spectral data set, and select a main color attribute spectral feature from the color attribute spectral features by using a principal 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 data set, and perform weighting processing on data of different dimensions in the main color attribute spectral features by using the optimal color matching function to obtain target spectral features;
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 characteristic 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, when the modules in the color difference adjustment apparatus 100 based on spectral analysis according to the embodiment of the present invention are used, the same technical means as the color difference adjustment method based on spectral analysis described in fig. 1 to 3 is adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a color difference 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, such as a color difference adjustment program based on spectral analysis, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a color difference adjustment program based on spectral analysis, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and 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 to store application software installed in the electronic device and various types of data, such as codes of a color difference adjustment program based on spectral analysis, etc., but also to temporarily store data that has been output or is to be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes 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.), which are 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), and optionally 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 device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 5 only shows an electronic device with components, and it will be 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 a combination of certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply 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 realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the embodiments described are illustrative only and are not to be construed as limiting the scope of the claims.
The color difference adjustment program based on spectral analysis stored in the memory 11 of the electronic device 1 is a combination of instructions, which when executed 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 spectral features of the initial spectral data set, and selecting main color attribute spectral features from the color attribute spectral features by utilizing a principal component analysis algorithm and an independent component analysis algorithm;
selecting an optimal color matching function according to the spectral radiance in the initial spectral data set, and performing weighting processing on data of different dimensions in the main color attribute spectral features by using the optimal color matching function to obtain target spectral features;
converting the target spectral feature into a predicted color feature value by using a pre-trained spectral mapping model;
and calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and performing color calibration on the display screen to be tested according to the color difference.
Specifically, the specific implementation method of the processor 10 for the instruction may refer to the description of the relevant steps in the embodiment corresponding to the drawing, and is not repeated here.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, 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, may 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 spectral features of the initial spectral data set, and selecting main color attribute spectral features from the color attribute spectral features by utilizing a principal component analysis algorithm and an independent component analysis algorithm;
selecting an optimal color matching function according to the spectral radiance in the initial spectral data set, and performing weighting processing on data of different dimensions in the main color attribute spectral features by using the optimal color matching function to obtain target spectral features;
converting the target spectral features into predicted color feature values by using a pre-trained spectral mapping model;
and calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and performing color calibration on the display screen to be tested according to the color difference.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
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 attributes 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 block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for adjusting chromatic aberration 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, and selecting main color attribute spectral features from the color attribute spectral features by utilizing a principal component analysis algorithm and an independent component analysis algorithm;
selecting an optimal color matching function according to the spectral radiance in the initial spectral data set, and performing weighting processing on data of different dimensions in the main color attribute spectral features by using the optimal color matching function to obtain target spectral features;
converting the target spectral feature into a predicted color feature value by using a pre-trained spectral mapping model;
and calculating the color difference between the predicted color characteristic value and the standard chromaticity value set, and performing color calibration on the display screen to be tested according to the color difference.
2. The method of claim 1, wherein selecting a dominant color attribute spectral feature from the color attribute spectral features using a principal component analysis algorithm and an independent component analysis algorithm comprises:
sorting the color attribute spectral features according to the accumulated contribution rate by utilizing a principal component analysis algorithm, and selecting the color attribute spectral features of which the accumulated contribution rate is greater than a preset threshold value as initial main color attribute spectral features;
performing matrix transformation on the 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 a spectral reflectance in the initial spectral data set and the predicted spectral reflectance;
analyzing the spectral reflection error value by using the independent component analysis algorithm to obtain an updated spectral reflection error value;
and reconstructing the initial main color attribute spectral characteristics by using the updated spectral reflection error value to obtain the main color attribute spectral characteristics.
3. The method of spectral analysis-based color difference adjustment according to claim 1, wherein said selecting an optimal color matching function according to spectral radiance in the initial spectral data set comprises:
weighting the spectral radiance in the initial spectral data set by using each color matching function in a preset color matching function set to obtain a weighted spectral radiance set;
respectively predicting a prediction RGB digital driving value corresponding to each weighted spectral radiance in the weighted spectral radiance set by using a preset spectral characterization model;
acquiring a real RGB digital driving value corresponding to each weighted spectral radiance in the weighted spectral radiance set, and calculating a difference value between the predicted RGB digital driving value and the real RGB digital driving value;
and selecting the color matching function corresponding to the prediction RGB digital driving value with the minimum difference value as the optimal color matching function.
4. The method of spectral analysis-based color difference adjustment according to claim 1, wherein the converting the target spectral feature into a predicted color feature value using a pre-trained spectral mapping model comprises:
according to the weight of an input layer in a pre-trained spectrum mapping model and the bias of a hidden layer, performing weighted calculation on the target spectrum characteristics by using an activation function of the hidden layer in the spectrum mapping model to obtain the output characteristics of each hidden layer;
and according to the weight of the output layer in the pre-trained spectral mapping model, performing weighted calculation on the output characteristic of each hidden layer to obtain a predicted color characteristic value.
5. The method for spectral analysis-based color difference adjustment according to claim 4, wherein before converting the target spectral feature into a predicted color feature value using a pre-trained spectral mapping model, the method further comprises:
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 spectral features of the historical spectral data set, selecting an optimal color matching function to perform weighting processing on data of different dimensions in the historical main color attribute spectral features to obtain historical target spectral features;
randomly generating weights of input layers and bias of hidden layers in a plurality of pre-constructed spectral mapping models to obtain a weight set of the input layers and a bias set of the hidden layers;
and predicting the historical target spectral features respectively 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 a 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.
6. The method for spectral analysis-based color difference adjustment according to claim 1, wherein said extracting color property spectral features of said initial spectral data set comprises:
screening abnormal data in the spectrum data set by using a Mahalanobis 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 wave band corresponding to the color of visible light 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 to be used as color attribute spectrum characteristics of the display screen to be tested.
7. The method for adjusting color difference based on spectral analysis according to claim 1, wherein the obtaining an initial spectral data set and a corresponding standard chromaticity value set of RGB channels of a display screen to be tested comprises:
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 a calibrated display screen to be tested by using a preset color block generation method according to a first preset interval value and RGB digital driving values of a first preset condition, and acquiring spectral data sets with 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 calibrated display screen to be tested by using the color block generating method according to a second preset interval value and RGB digital driving values of a second preset condition, and acquiring spectral data sets with different wavelengths of a visible spectrum of the second color block set and corresponding standard chromaticity value sets;
and combining the spectral data set of the first color block set and the spectral data set of the second color block set to obtain an initial spectral data set of the RGB channel of the display screen to be tested, and combining 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.
8. A chromatic aberration adjustment apparatus based on spectral analysis, the apparatus comprising:
the spectrum data acquisition module is used for acquiring an initial spectrum data set and a corresponding standard chromaticity value set of an RGB channel of the display screen to be tested;
the spectral feature selection module is used for extracting color attribute spectral features of the initial spectral data set and selecting main color attribute spectral features from the color attribute spectral features by utilizing a principal component analysis algorithm and an independent component analysis algorithm;
the spectral feature matching module is used for selecting an optimal color matching function according to the spectral radiance in the initial spectral data set, and performing weighting processing on data with different dimensions in the main color attribute spectral features by using the optimal color matching function to obtain target spectral features;
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.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
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 method of spectral analysis based color difference adjustment according to any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the spectral analysis-based color difference adjustment method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211317947.6A CN115547270B (en) | 2022-10-26 | 2022-10-26 | Color difference adjustment method, device, equipment and storage medium based on spectrum analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211317947.6A CN115547270B (en) | 2022-10-26 | 2022-10-26 | Color difference adjustment method, device, equipment and storage medium based on spectrum analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115547270A true CN115547270A (en) | 2022-12-30 |
| CN115547270B CN115547270B (en) | 2023-05-09 |
Family
ID=84718697
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211317947.6A Active CN115547270B (en) | 2022-10-26 | 2022-10-26 | Color difference adjustment method, device, equipment and storage medium based on spectrum analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115547270B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116229856A (en) * | 2023-05-10 | 2023-06-06 | 山西晋聚轩科技有限公司 | Automatic control screen detection system and method for computer |
| CN116754511A (en) * | 2023-08-18 | 2023-09-15 | 天津博霆光电技术有限公司 | Indocyanine green detection method and system based on spectrum technology |
| CN117349683A (en) * | 2023-12-04 | 2024-01-05 | 广东台塑智能科技有限公司 | Auto-parts application colour difference anomaly detection system based on spectral data |
| CN117379033A (en) * | 2023-12-13 | 2024-01-12 | 深圳市宗匠科技有限公司 | Skin pigment detection method, device, computer equipment and storage medium |
| CN117896867A (en) * | 2024-03-14 | 2024-04-16 | 深圳市正远科技有限公司 | Method and system for realizing display color regulation and control of LED light source based on response |
| CN118158374A (en) * | 2024-05-11 | 2024-06-07 | 荣耀终端有限公司 | Color correction method and related equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE112010005744T5 (en) * | 2010-07-16 | 2013-06-27 | Robert Bosch Gmbh | Method for the detection and correction of a lateral chromatic aberration |
| CN109903256A (en) * | 2019-03-07 | 2019-06-18 | 京东方科技集团股份有限公司 | Model training method, chromatic aberration calibrating method, device, medium and electronic equipment |
| CN110135523A (en) * | 2019-05-29 | 2019-08-16 | 上海出版印刷高等专科学校 | Method for building up, system, storage medium and the electronic equipment of rebuilding spectrum model |
| JP2020020609A (en) * | 2018-07-30 | 2020-02-06 | 日本放送協会 | Color filter for magnification chromatic aberration measurement and magnification chromatic aberration measurement device using the same |
| CN112254814A (en) * | 2020-10-21 | 2021-01-22 | 北京印刷学院 | Method and device for constructing multi-dimensional spectral color space and electronic equipment |
-
2022
- 2022-10-26 CN CN202211317947.6A patent/CN115547270B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE112010005744T5 (en) * | 2010-07-16 | 2013-06-27 | Robert Bosch Gmbh | Method for the detection and correction of a lateral chromatic aberration |
| JP2020020609A (en) * | 2018-07-30 | 2020-02-06 | 日本放送協会 | Color filter for magnification chromatic aberration measurement and magnification chromatic aberration measurement device using the same |
| CN109903256A (en) * | 2019-03-07 | 2019-06-18 | 京东方科技集团股份有限公司 | Model training method, chromatic aberration calibrating method, device, medium and electronic equipment |
| US20200288092A1 (en) * | 2019-03-07 | 2020-09-10 | Beijing Boe Display Technology Co., Ltd. | Model training method, chromatic aberration correction method, apparatus, medium and electronic device |
| CN110135523A (en) * | 2019-05-29 | 2019-08-16 | 上海出版印刷高等专科学校 | Method for building up, system, storage medium and the electronic equipment of rebuilding spectrum model |
| CN112254814A (en) * | 2020-10-21 | 2021-01-22 | 北京印刷学院 | Method and device for constructing multi-dimensional spectral color space and electronic equipment |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116229856A (en) * | 2023-05-10 | 2023-06-06 | 山西晋聚轩科技有限公司 | Automatic control screen detection system and method for computer |
| CN116754511A (en) * | 2023-08-18 | 2023-09-15 | 天津博霆光电技术有限公司 | Indocyanine green detection method and system based on spectrum technology |
| CN116754511B (en) * | 2023-08-18 | 2023-10-27 | 天津博霆光电技术有限公司 | Indocyanine green detection method and system based on spectrum technology |
| CN117349683A (en) * | 2023-12-04 | 2024-01-05 | 广东台塑智能科技有限公司 | Auto-parts application colour difference anomaly detection system based on spectral data |
| CN117349683B (en) * | 2023-12-04 | 2024-02-02 | 广东台塑智能科技有限公司 | Auto-parts application colour difference anomaly detection system based on spectral data |
| CN117379033A (en) * | 2023-12-13 | 2024-01-12 | 深圳市宗匠科技有限公司 | Skin pigment detection method, device, computer equipment and storage medium |
| CN117379033B (en) * | 2023-12-13 | 2024-02-20 | 深圳市宗匠科技有限公司 | Skin pigment detection method, device, computer equipment and storage medium |
| CN117896867A (en) * | 2024-03-14 | 2024-04-16 | 深圳市正远科技有限公司 | Method and system for realizing display color regulation and control of LED light source based on response |
| CN117896867B (en) * | 2024-03-14 | 2024-05-24 | 深圳市正远科技有限公司 | Method and system for realizing display color regulation and control of LED light source based on response |
| CN118158374A (en) * | 2024-05-11 | 2024-06-07 | 荣耀终端有限公司 | Color correction method and related equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115547270B (en) | 2023-05-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115547270B (en) | Color difference adjustment method, device, equipment and storage medium based on spectrum analysis | |
| CN109903256B (en) | Model training method, chromatic aberration correction device, medium, and electronic apparatus | |
| CN103426391B (en) | For the method and apparatus of display calibration | |
| CN107346653B (en) | GAMMA curve adjusting method and device based on deep learning | |
| CN103765503A (en) | Fast calibration of displays using spectral-based colorimetrically calibrated multicolor camera | |
| CN101022501A (en) | Image processing apparatus, image processing method, computer readable medium and data signal | |
| Asano et al. | Categorical observers for metamerism | |
| CN101325648B (en) | Color processing apparatus, color processing method | |
| CN113316711B (en) | Method and apparatus for estimating ambient light | |
| JP2000293129A (en) | Display characteristic recognizing device, display characteristic recognizing program recording medium, display characteristic adjusting device and display characteristic adjusting program recording medium | |
| US20210219700A1 (en) | Method for simulating the rendering of a make-up product on a body area | |
| CN114512085A (en) | Visual color calibration method of TFT (thin film transistor) display screen | |
| CN115311167B (en) | Color gamut control method, device and equipment based on multicolor light and storage medium | |
| CN113189021A (en) | Method for identifying rock color based on spectrum | |
| KR20230099610A (en) | Method, apparatus and electronic device for estimating screen light leakage amount | |
| KR100755680B1 (en) | Apparatus and method for color conversion | |
| CN107358255A (en) | GAMMA based on convolutional neural networks ties up a classification learning method and device | |
| CN112016621B (en) | Training method of classification model, color classification method and electronic equipment | |
| CN115278186B (en) | Controllable uniform projection method, device, equipment and medium based on Internet of things | |
| CN112767038A (en) | Poster CTR prediction method and device based on aesthetic characteristics | |
| CN115979419B (en) | Ambient light detection method, device, equipment and storage medium | |
| US11962949B2 (en) | Method of air pollution estimation based on spectral image processing | |
| CN115588405A (en) | Display parameter adjusting method, device, equipment and storage medium | |
| CN112801216B (en) | Wavelength compensation method and device, computer equipment and storage medium | |
| CN103257834A (en) | Systems and methods for facilitating reproduction of arbitrary colors |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |