CN107424197B - Method for realizing cross-media color reproduction based on spectral domain mapping - Google Patents
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Abstract
The invention relates to the technical field of spectral gamut mapping, in particular to a method for realizing cross-media color reproduction based on spectral gamut mapping. It includes: 1) constructing a virtual light source; 2) establishing a chromaticity color gamut space; 3) determining whether the CIE Lab value is within the chromaticity gamut space of the target device: 4) calculating a CIE Lab value tristimulus value corresponding to each multispectral image sample point in the chromaticity color gamut space of the digital imaging equipment under other virtual light sources; 5) the alignment is performed to form a CIE Lab set. The invention fully considers the requirement of actual production and selects the corresponding space dimension; an ICS for solving the problem of poor chromatic aberration robustness during light source conversion is constructed by using a virtual light source, and a spectrum domain description method and a spectrum domain mapping method are designed according to the characteristics of the ICS; the color gamut visualization degree is high, the space uniformity is good, the establishment of a spectrum color management system is facilitated, and the use by a user is more convenient.
Description
Technical Field
The invention relates to the technical field of spectral gamut mapping, in particular to a method for realizing cross-media color reproduction based on spectral gamut mapping.
Background
With the rapid development and widespread use of digital imaging devices such as displays, scanners, mobile terminals, digital cameras, and the like, cross-media color reproduction has become unavoidable in everyday life. However, the color generation mechanism, physical characteristics, and the like of different digital imaging devices are inconsistent, and there is a large difference between the color ranges (referred to as color gamut) that can be represented, resulting in distortion of color reproduction across media, which seriously affects the conversion and reproduction of color information between devices. Gamut mismatch between diverse digital imaging devices is the most dominant factor causing color image distortion across media color reproduction; therefore, in the process of cross-media color reproduction, color gamut mapping is always the core problem of cross-media color reproduction research at home and abroad.
Color Gamut Mapping (Color Gamut Mapping) is a technique for converting colors in other media into a Gamut range that can be presented by a target device, and is classified into two types, i.e., chroma-based Gamut Mapping and spectral-based Gamut Mapping (spectral Gamut Mapping for short) according to a Color information reproduction medium. The color gamut mapping based on the chromaticity is based on the chromaticity value obtained by integrating the spectral characteristic of the surface of an object, the irradiation light source and the visual characteristic of an observer, when the external irradiation light source or the observer changes, the external irradiation light source and the observer are not equivalent, the external irradiation light source or the observer belongs to the category of metamerism, and the problem that the colors are not matched with the changes of the light source and the observer, namely the problem of metamerism, cannot be solved. Spectral-based gamut mapping uses spectral reflectance, called object "fingerprint", as a color medium, and can realize matching, i.e., unconditional mutual replacement, between a copy color and an original color under any observation environment and light source, independently of the external observation environment, including light source, observer, ambient environment, and other factors. The spectral gamut mapping is a fundamental way to actually realize cross-media color reproduction, and brings essential improvement to the current cross-media color reproduction.
Spectral gamut mapping techniques include selection of device-independent color spaces, spectral gamut boundary description, and spectral gamut mapping methods. Because the spectral data is multi-dimensional data, when the spectral data is used for cross-media color reproduction and color transfer, the problems of data redundancy, large calculation amount, large storage space and the like exist, great difficulty is brought to description and visualization of a spectral domain and selection of a mapping direction, and spectral reflectivity is not suitable for directly performing spectrum-image processing, spectral domain description and spectral domain mapping. Therefore, the selection of the device-independent color space directly determines the spectral gamut description and the spectral gamut mapping method, which is a precondition for realizing cross-media spectral color reproduction.
The cross-media-based spectral domain mapping technology is performed in an Inter Connection Space (ICS) based on a data dimension reduction principle. ICS is divided into two types according to the data dimension reduction principle: one is based on multivariate statistical analysis and the other is based on metamerism black compensation. The multivariate statistical analysis method is mainly used for reducing the dimension of the spectral data by utilizing a multivariate statistical theory based on the correlation among the spectral data or the correlation among wave bands, and the research methods mainly comprise a Principal Component Analysis (PCA), an independent principal component analysis (IDA), a non-negative principal component analysis (NTA) and the like; the dimensionality reduction result obtained by the method is only a mathematically optimal solution, but not optimal matching and reduction in human eye vision, and the corresponding coefficient space is a non-uniform space. Therefore, spectral gamut mapping using this model is not widely accepted.
The metamerism-based black compensation method is mainly characterized in that the metamerism-based black compensation method can be decomposed into a basic spectrum and a metamerism black spectrum according to color spectrum information; the basic spectrum determines the chromaticity information of the spectrum, while the metameric black spectrum determines the spectral accuracy of the spectrum, determining the final reconstructed spectrum from a set of spectra having the same chromaticity; the main algorithms include LabPQR and improved models (LabRGB, wLabPQR, XYZLMS, LabLabLabLab and the like) according to the LabPQR characteristics. The model is composed of a chromaticity color gamut space and a metamerism black-and-white space, can simultaneously consider the traditional color management system and keep the spectral information of the color, so the model is widely applied to the aspect of spectral color processing in the space. However, the ICS constructed based on metamerism black theory above is a Nested (Nested) space, resulting in poor color gamut visualization and spatial uniformity; meanwhile, the ICS needs to be constructed under a fixed light source, and there is a problem that the color difference robustness is poor when the light source is changed.
Disclosure of Invention
In order to make up for the defects of the prior art, the invention provides a method for realizing cross-media color reproduction based on spectral domain mapping, which eliminates the defects of poor light source transformation robustness, high color gamut visualization degree, good space uniformity and different space dimensions.
The invention is realized by the following technical scheme:
a method for enabling cross-media color reproduction based on spectral gamut mapping, comprising the steps of:
1) extracting principal components of different light source spectral characteristics by adopting a multivariate statistical analysis method, and constructing virtual light sources with required quantity by adopting a numerical value transformation mode;
2) collecting spectral reflectivity of a multispectral image and spectral reflectivity of colors contained in a maximized spectral domain of the digital imaging device; calculating the spectral reflectivity of the multispectral image and the CIE Lab value corresponding to the spectral reflectivity of the digital imaging equipment under the virtual light source; then color gamut boundary description based on chromaticity is carried out according to the CIE Lab value of the digital imaging equipment, and a chromaticity color gamut space of the digital imaging equipment is established;
3) judging whether the CIE Lab value of the multispectral image sample point is within the chromaticity color gamut space of the target equipment:
(a) if the sample point is not contained in the chromaticity color gamut space of the target equipment, the sample point is judged not to be in the spectral gamut range of the digital imaging equipment; mapping is carried out in the chromaticity color gamut space of the digital imaging equipment according to the CIE Lab value corresponding to the multispectral image sample point, and the most suitable mapping point is found;
(b) if the color gamut is contained in the chromaticity color gamut space of the target device, the CIE Lab value corresponding to the multispectral image sample point is kept unchanged.
4) According to actual production requirements and the difference of the number of virtual light sources, calculating CIE Lab value tristimulus values corresponding to each multispectral image sample point in the chromaticity color gamut space of the digital imaging equipment under other virtual light sources according to the step 3);
5) arranging according to the corresponding CIE Lab value of the multispectral image sample points in the chromaticity color gamut space of the digital imaging equipment and the light source sequence formed by the principal components to form a CIE Lab set; and finally, obtaining a mapping spectrum by the CIE Lab set of the multispectral image sample points through a multi-light source basic spectrum compensation model.
The specific operation steps of the step 1) are as follows:
(a) normalization processing is carried out on the relative spectral power distribution of different light sources according to the maximum power value of each light source, and a light source spectral power normalization set is obtained;
(b) extracting principal components of the light source spectrum normalization set by adopting a multivariate statistical analysis method;
(c) normalizing each principal component to linearly convert the principal component into a numerical range of [0,1] to obtain a converted principal component; selecting different conversion principal components as virtual light sources according to the principal component contribution rate according to the requirements;
the formula for obtaining the conversion principal component through normalization processing is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing the maximum value and the minimum value of each principal component value.
The specific operation steps of the step 2) are as follows:
(a) collecting spectral reflectivity of a multispectral image and spectral reflectivity of colors contained in a maximized spectral domain of the digital imaging device;
(b) calculating the CIE Lab value corresponding to the spectral reflectivity of the multispectral image and the spectral reflectivity of the color contained in the maximized spectral domain of the digital imaging equipment under the virtual light source:
wherein k is an adjustment factor,
is the spectral reflectivity of the surface of the object,
to visualize the relative spectral power distribution of the light source,
the color point is a CIE color matching function, CIE XYZ is obtained by integration in a visible range of a spectrum, and a CIE Lab value of a color point can be obtained by CIEXYZ value conversion;
(c) and converting the Lab value of the target equipment into a CIE LCH color space, performing color gamut boundary description based on chromaticity in the CIE LCH color space by adopting partition maximum boundary description, and establishing the chromaticity color gamut space of the target equipment.
The specific operation steps of the step 5) are as follows:
(a) recording the CIE Lab values corresponding to different virtual light sources in the chromaticity color gamut space of the digital imaging equipment as
Arranging the light sources formed by the main components in sequence to form a CIE Lab set:
(b) converting CIE Lab set C to normalized color values
Calculating a conversion matrix H between the set of spectral reflectivities of the colors contained in the maximized spectral domain of the digital imaging device and their normalized color values, i.e.
R is a spectral reflectance set of colors contained in a maximized spectral domain of the digital imaging device;
(c) arranging according to the corresponding CIE Lab value of the multispectral image sample points in the chromaticity color gamut space of the digital imaging equipment and the light source sequence formed by the main components to form a CIE Lab group; conversion of CIE Lab sets to normalized color values
(ii) a From
Reconstructing spectral reflectance
：
The multivariate statistical analysis method refers to a principal component analysis method, an independent principal component analysis method or a non-negative principal component analysis method.
The light source refers to CIE illuminants, LEDs, TH or fluorescent lamps.
The invention has the beneficial effects that:
the invention fully considers the requirement of actual production and selects the corresponding space dimension; an ICS for solving the problem of poor chromatic aberration robustness during light source conversion is constructed by using a virtual light source, and a spectrum domain description method and a spectrum domain mapping method are designed according to the characteristics of the ICS; the color gamut visualization degree is high, the space uniformity is good, the establishment of a spectrum color management system is facilitated, and the use by a user is more convenient.
Drawings
The invention is further described with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of a method for establishing a spectral gamut mapping-based cross-media color reproduction;
FIG. 2 is a schematic diagram of an embodiment of a method for describing a maximum boundary of a partition (SMGBD) in a CIELAB space;
fig. 3 is a schematic diagram of the gamut mapping direction in the embodiment.
Detailed Description
The drawings illustrate specific embodiments of the invention. As shown in fig. 1 to 3, the method for implementing cross-media color reproduction based on spectral gamut mapping includes the following steps:
1) extracting principal components of spectral characteristics of light sources such as different CIE illuminants, LEDs, TH and fluorescent lamps by adopting a multivariate statistical analysis method (such as a principal component analysis method, an independent principal component analysis method, a non-negative principal component analysis method and the like), and constructing virtual light sources with required quantity by adopting a numerical value conversion mode; the specific operation steps are as follows:
(a) relative spectral power distribution to different CIE illuminants, LEDs, TH and fluorescent lamps
Normalizing according to the maximum power value of each light source to obtain a light source spectral power normalization set
(ii) a Normalization processing to normalized lightSource spectral power set
The formula of (1) is:
in the formula (I), the compound is shown in the specification,
respectively representing the maximum value in the vertical direction of each principal component;
(b) extracting principal components of the light source spectrum normalization set by adopting a multivariate statistical analysis method (such as a principal component analysis method, an independent principal component analysis method, a non-negative principal component analysis method and the like):
in the formula (I), the compound is shown in the specification,
is the ith feature vector and is the ith feature vector,
is the coefficient corresponding to the ith characteristic vector;
(c) since the main component contains negative values, it is necessary to apply to each main component
Performing normalization to linearly convert the principal component into a value range of [0,1]]Obtaining a converted principal component
(ii) a In order to meet the requirements, different conversion principal components are selected as virtual light sources according to the contribution rate of the principal components
；
The formula for obtaining the conversion principal component through normalization processing is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing the maximum value and the minimum value of each principal component value.
2) Collecting spectral reflectivity of a multispectral image and spectral reflectivity of colors contained in a maximized spectral domain of the digital imaging device; calculating the spectral reflectivity of the multispectral image and the CIE Lab value corresponding to the spectral reflectivity of the digital imaging equipment under the virtual light source; then color gamut boundary description based on chromaticity is carried out according to the CIE Lab value of the digital imaging equipment, and a chromaticity color gamut space of the digital imaging equipment is established; the specific operation is as follows:
(a) collecting spectral reflectivity of a multispectral image and spectral reflectivity of colors contained in a maximized spectral domain of the digital imaging device;
(b) calculating the CIE Lab value corresponding to the spectral reflectivity of the multispectral image and the spectral reflectivity of the color contained in the maximized spectral domain of the digital imaging equipment under the virtual light source:
wherein k is an adjustment factor,
is the spectral reflectivity of the surface of the object,
to visualize the relative spectral power distribution of the light source,
the color point is a CIE color matching function, CIE XYZ is obtained by integration in a visible range of a spectrum, and a CIE Lab value of a color point can be obtained by CIEXYZ value conversion;
(c) converting the Lab value of the digital imaging device into a CIE LCH color space, performing color gamut boundary description based on chromaticity in the CIE LCH color space by adopting Segment maximum boundary description (SMGBD), and establishing the chromaticity color gamut space of the target device.
3) Judging whether the CIE Lab value of the multispectral image sample point is within the chromaticity color gamut space of the target imaging equipment:
(a) if the sample point is not contained in the chromaticity color gamut space of the target equipment, the sample point is judged not to be in the spectral gamut range of the digital imaging equipment; mapping is carried out in the chromaticity color gamut space of the digital imaging equipment according to the CIE Lab value corresponding to the multispectral image sample point, and the most suitable mapping point is found;
(b) if the color gamut is contained in the chromaticity color gamut space of the target device, the CIE Lab value corresponding to the multispectral image sample point is kept unchanged.
4) According to actual production requirements and the difference of the number of virtual light sources, calculating CIE Lab value tristimulus values corresponding to each multispectral image sample point in the chromaticity color gamut space of the digital imaging equipment under other virtual light sources according to the step 3);
5) arranging according to the corresponding CIE Lab value of the multispectral image sample points in the chromaticity color gamut space of the digital imaging equipment and the light source sequence formed by the principal components to form a CIE Lab set; and finally, obtaining a mapping spectrum by a CIE Lab set of the multispectral image sample points through a multi-light-source basic spectrum compensation model, wherein the specific operation steps are as follows:
(a) recording the CIE Lab values corresponding to different virtual light sources in the chromaticity color gamut space of the digital imaging equipment as
Arranging the light sources formed by the main components in sequence to form a CIE Lab set:
(b) converting CIE Lab set C to normalized color values
Calculating a conversion matrix H between the set of spectral reflectivities of the colors contained in the maximized spectral domain of the digital imaging device and their normalized color values, i.e.
R is a spectral reflectance set of colors contained in a maximized spectral domain of the digital imaging device;
(c) arranging according to the corresponding CIE Lab value of the multispectral image sample points in the chromaticity color gamut space of the digital imaging equipment and the light source sequence formed by the main components to form a CIE Lab group; conversion of CIE Lab sets to normalized color values
(ii) a From
Reconstructing spectral reflectance
：
Other technical features than those described in the specification are known to those skilled in the art.
Claims (3)
1. A method for enabling cross-media color reproduction based on spectral gamut mapping, comprising the steps of:
1) extracting principal components of different light source spectral characteristics by adopting a multivariate statistical analysis method, and constructing virtual light sources with required quantity by adopting a numerical value transformation mode; the specific operation steps are as follows:
(a) normalization processing is carried out on the relative spectral power distribution of different light sources according to the maximum power value of each light source, and a light source spectral power normalization set is obtained;
(b) extracting principal components of the light source spectrum normalization set by adopting a multivariate statistical analysis method;
(c) normalizing each principal component to linearly convert the principal component into a numerical range of [0,1] to obtain a converted principal component; selecting different conversion principal components as virtual light sources according to the principal component contribution rate according to the requirements;
the formula for obtaining the conversion principal component through normalization processing is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing the maximum value and the minimum value of each principal component value;
2) collecting spectral reflectivity of a multispectral image and spectral reflectivity of colors contained in a maximized spectral domain of the digital imaging device; calculating the spectral reflectivity of the multispectral image and the CIE Lab value corresponding to the spectral reflectivity of the digital imaging equipment under the virtual light source; then color gamut boundary description based on chromaticity is carried out according to the CIE Lab value of the digital imaging equipment, and a chromaticity color gamut space of the digital imaging equipment is established; the specific operation steps are as follows:
(a) collecting spectral reflectivity of a multispectral image and spectral reflectivity of colors contained in a maximized spectral domain of the digital imaging device;
(b) calculating the CIE Lab value corresponding to the spectral reflectivity of the multispectral image and the spectral reflectivity of the color contained in the maximized spectral domain of the digital imaging equipment under the virtual light source:
wherein k is an adjustment factor,
is the spectral reflectivity of the surface of the object,
to visualize the relative spectral power distribution of the light source,
the color point is a CIE color matching function, CIE XYZ is obtained by integration in a visible range of a spectrum, and a CIE Lab value of a color point can be obtained by CIEXYZ value conversion;
(c) converting the Lab value of the target equipment into a CIE LCH color space, performing color gamut boundary description based on chromaticity in the CIE LCH color space by adopting partition maximum boundary description, and establishing the chromaticity color gamut space of the target equipment;
3) judging whether the CIE Lab value of the multispectral image sample point is within the chromaticity color gamut space of the target equipment:
(a) if the sample point is not contained in the chromaticity color gamut space of the target equipment, the sample point is judged not to be in the spectral gamut range of the digital imaging equipment; mapping is carried out in the chromaticity color gamut space of the digital imaging equipment according to the CIE Lab value corresponding to the multispectral image sample point, and the most suitable mapping point is found;
(b) if the color gamut is contained in the chromaticity color gamut space of the target equipment, the CIE Lab value corresponding to the multispectral image sample point is kept unchanged;
4) according to actual production requirements and the difference of the number of virtual light sources, calculating CIE Lab value tristimulus values corresponding to each multispectral image sample point in the chromaticity color gamut space of the digital imaging equipment under other virtual light sources according to the step 3);
5) arranging according to the corresponding CIE Lab value of the multispectral image sample points in the chromaticity color gamut space of the digital imaging equipment and the light source sequence formed by the principal components to form a CIE Lab set; and finally, obtaining a mapping spectrum by the CIE Lab set of the multispectral image sample points through a multi-light source basic spectrum compensation model, wherein the specific operation steps are as follows:
(a) recording the CIE Lab values corresponding to different virtual light sources in the chromaticity color gamut space of the digital imaging equipment as
Arranging the light sources formed by the main components in sequence to form a CIE Lab set:
(b) converting CIE Lab set C to normalized color values
Calculating a conversion matrix H between the set of spectral reflectivities of the colors contained in the maximized spectral domain of the digital imaging device and their normalized color values, i.e.
R is a spectral reflectance set of colors contained in a maximized spectral domain of the digital imaging device;
(c) arranging according to the corresponding CIE Lab value of the multispectral image sample points in the chromaticity color gamut space of the digital imaging equipment and the light source sequence formed by the main components to form a CIE Lab group; conversion of CIE Lab sets to normalized color values
(ii) a From
Reconstructing spectral reflectance
：
2. The method of claim 1, wherein the multivariate statistical analysis is principal component analysis, independent principal component analysis, or non-negative principal component analysis.
3. The method of claim 1, wherein the light source is CIE illuminant, LED, TH or fluorescent lamp.
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| CN109727213B (en) * | 2018-12-27 | 2021-01-19 | 苏州易泰勒电子科技有限公司 | Electronic display label image reproduction method based on visual perception |
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