CN107357537B - Method and system for constructing color separation models of halftone equipment in batch for different media - Google Patents
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Abstract
The invention relates to a method and a system for constructing color separation models of halftone equipment in batches for different media, which comprises the steps of uniformly sampling in an equipment ink volume space to obtain a low-density sample set to prepare color samples; measuring and obtaining color information of each medium color sample and calculating a corresponding color gamut; selecting a medium with the largest color gamut, performing high-density sampling and constructing a high-precision characterization model which comprises a forward prediction model and a reverse color separation model; solving the maximum color gamut medium ink amount information corresponding to the color information of other medium color samples by using a reverse color separation model; constructing a neural network model between the ink quantity information and the original low-density sampling ink quantity information; for any color to be copied, the corresponding ink amount information is solved by using a maximum color gamut medium reverse color separation model, and then the ink amount information aiming at other media is predicted according to the neural network model to complete color separation. The invention can realize the batch construction of color separation models facing different media and is convenient to implement.
Description
Technical Field
The invention belongs to the technical field of halftone color reproduction, and particularly relates to a method and a system for batch construction of different medium-oriented halftone equipment color separation models.
Background
Halftone color reproduction technology is the mainstream technology in the field of image color reproduction at present, and accurate reproduction of colors is realized through density arrangement and superposition of halftone dots. In the technology, the construction of a halftone equipment color separation model is a key link in the color reproduction process, and is essentially the construction of a mapping model from color information to be reproduced to halftone equipment ink amount information.
At present, in the field of halftone color reproduction technology, the construction of color separation models is based on the construction of halftone prediction models. Wherein, the halftone color prediction model means a mapping function from halftone ink amount information to sample color information. It can be seen that the color separation model and the color prediction model are actually inverse processes of each other, and in the research field, the color prediction model is usually referred to as a forward model for short, the color separation model is referred to as a reverse model for short, and the process of integrating the color separation model and the color prediction model is referred to as halftone equipment characterization modeling. In practice, those skilled in the art typically use a halftone apparatus to prepare color samples for specific ink volume information, measure their color information, and construct a forward model therefrom. Then, the forward model is mathematically inverted using a correlation optimization algorithm to construct a reverse model, i.e., a color separation model.
At present, in order to ensure the construction accuracy of a halftone color separation model, a high-density sampling method is generally adopted in the existing method, such as references 1 and 2.
Reference 1: wang B, Xu H, Luo MR, Guo J.Spectral-based color registration method for a Multi-ink printer. Chinese Optics letters.2011; 9(6):063301.
Reference 2: liu Q, Wan X, Xie D.optimization of spectral printer based on a modified cellular Nielsen spectral Neugebauer model.J Opt Soc am.2014; 31(6):1284-94.
However, the high-density sampling method has significant defects in color sample production time, consumable cost, and the like, and thus has limited practical application value. Especially, when the halftone color reproduction medium has a large variety, it is not feasible to perform such color separation model construction on each medium one by one.
Therefore, a simpler and faster method is urgently needed in the field, and the batch construction of the halftone color separation models facing different media can be realized more accurately and efficiently, so that effective method support is provided for the application and popularization of the halftone color reproduction technology. However, due to the restriction of subjective and objective factors such as the level of theoretical methods, no solution has been proposed in the academic and industrial fields.
Disclosure of Invention
The invention aims to solve the problems in the background art and provides a method and a system for batch construction of color separation models of halftone equipment for different media.
The technical scheme of the invention is to provide a method for constructing color separation models of halftone equipment facing different media in batches, which comprises the following steps:
step 1, uniformly sampling in an ink color space of equipment to obtain a low-density sample set T, and preparing color samples for each medium;
step 2, measuring the color information of the color sample prepared in the step 1 by using color measuring equipment;
step 3, calculating the color gamut volume of each medium color sample set by using the color information obtained by measurement in the step 2;
step 4, selecting an optimal color gamut medium J based on a color gamut maximization principle, and constructing a high-density sample set G-based characterization model aiming at the medium J, wherein the characterization model comprises a forward prediction model F and a reverse color separation model B;
step 5, solving ink amount information T' corresponding to the information S of the color samples prepared in the step 1 in the other medium color sample set T by using the color model B in the step 4;
step 6, constructing a neural network model between the other medium color sample sets T in the step 1 and the sample set T' obtained by the color separation model B in the step 5 by using a neural network method;
and 7, for any color information to be copied, firstly solving the ink amount information aiming at the J medium by using the color separation model B in the step 4, and then solving the ink amount information aiming at other media in the step 1 by using the neural network constructed in the step 6 to finish color separation.
In step 1, the sampling mode of the low-density sample set T is spatially uniform sampling, and the number of samples sampled per ink color dimension is 4.
Also, the color information measured in step 2 may be chrominance information or spectral reflectance information.
And in the step 3, the color gamut volume calculation method is a convex hull algorithm or an alpha-shape algorithm.
And in the step 4, the high-density sampling mode is spatially uniform sampling, and the number of samples sampled in each ink dimension is not less than 5.
The invention provides a system for constructing color separation models of halftone equipment in batches facing different media, which comprises the following modules:
the low-density sampling module is used for uniformly sampling the ink color space of the equipment to obtain a low-density sample set T and preparing color samples aiming at each medium;
the color measuring module measures the color information of the color sample prepared by the low-density sampling module by using color measuring equipment;
the color gamut volume calculation module is used for calculating the color gamut volume of each medium color sample set by utilizing the color information obtained by the measurement of the color measurement module;
the optimal medium characterization module selects a color gamut optimal medium J on the principle of color gamut maximization, and constructs a characterization model based on a high-density sample set G aiming at the medium J, wherein the characterization model comprises a forward prediction model F and a reverse color separation model B;
the low-density sample color separation calculation module is used for solving ink amount information T' corresponding to the color information S prepared by the low-density sampling module from other medium sample sets T by using the color separation model B in the optimal medium characterization module;
the neural network construction module is used for constructing a neural network model between the other medium sample sets T and the sample set T' obtained by the color separation model B of the low-density sample color separation calculation module by using a neural network method;
and the final color separation module is used for solving the ink amount information aiming at the J medium by using a color separation model B in the optimal medium characterization module aiming at any color information to be copied, and then solving the ink amount information aiming at other media by using a neural network constructed by the neural network construction module to finish color separation.
Moreover, the sampling mode of the low-density sample set T in the low-density sampling module is spatially uniform sampling, and the number of samples sampled in each ink dimension is 4.
Also, the color information measured in the color measurement module may be chromaticity information or spectral reflectance information.
And the color gamut volume calculating method in the color gamut volume calculating module is a convex hull algorithm or an alpha-shape algorithm.
Moreover, the high-density sampling mode in the optimal medium characterization module is space uniform sampling, and the number of sampling samples in each ink dimension is not less than 5.
The invention provides a technical scheme for batch construction of color separation models of different-medium-oriented halftone equipment, which is based on high-density sampling and high-precision modeling of a maximum color gamut medium, and realizes equivalent conversion of other medium color separation models based on the maximum color gamut medium color separation model by constructing a correlation model of the maximum color gamut medium and ink quantity information of other media, thereby realizing batch construction of high-precision color separation models of other media in a low-density sampling mode. The method effectively solves the problems in the background art, is convenient to implement, and has strong applicability in the technical field of multicolor halftone color reproduction. The technical scheme of the invention has important application significance and is supported by a plurality of research projects: 1. shenzhen city basic research project JCYJ20150422150029093, 2. national science fund project 61505149, 3. Wuhan city youth morning light talent project 2016070204010111, 4. Hubei province natural science fund project 2015CFB 204. The technical scheme of the invention is protected, and the method has important significance for the international leading position competition of related industries in China.
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FIG. 1 is a flow chart of an embodiment of a construction method of the present invention;
FIG. 2 is a block diagram of a construction system according to an embodiment of the present invention.
Detailed Description
The following provides a detailed description of embodiments of the invention, taken in conjunction with the accompanying drawings.
The method for constructing color separation models of halftone equipment facing different media in batch provided by the embodiment shown in fig. 1 can conveniently and efficiently realize the batch construction of color separation models of halftone equipment facing multiple media, and further promote the application and popularization of the color reproduction technology of halftone equipment at the present stage. In the embodiment, a CMYK four-color inkjet printer is taken as an example, and color separation models are respectively constructed by using highlight photographic paper H1, matt photographic paper H2 and rough art paper H3 of certain brands. It should be noted that the present invention is not limited to the halftone apparatus and medium described above, and the present method is also applicable to other apparatuses and media.
When the technical scheme of the invention is implemented, the technical scheme can be automatically operated by a person skilled in the art by adopting a computer software technology. The method flow provided by the embodiment comprises the following steps:
step 1, uniformly sampling in an ink color space of equipment to obtain a low-density sample set T, and preparing a color sample aiming at each medium. In step 1, the sampling mode of the low-density sample set T is spatial uniform sampling, and the number of sampling samples in each ink dimension is 4;
for the four-color printing apparatus of the embodiment, the low-density spatial uniform sampling is performed in the CMYK ink color space, where the sampling dimension of each ink color dimension is 4, that is, each ink color dimension takes a value of 0,33,67, and 100, so that 4 × 4 — 256 ink color samples are generated altogether, and a low-density sample set T is formed, and color samples are printed and prepared on various media.
Step 2, measuring the color information of the color sample prepared in the step 1 by using color measuring equipment, wherein the color information measured in the step 2 can be chrominance information or spectral reflectivity information;
and (3) measuring the spectral reflectivity information of each sample prepared in the step (1) by using an X-rite Spectroscopy type push-broom spectrophotometer, wherein the wavelength range of the spectral reflectivity information is 400-700 nm, and in the measuring process, in order to improve the measuring precision, the averaging mode of measuring for 2 times is adopted.
And 3, calculating the color gamut volume of each medium color sample set by using the color information obtained by measurement in the step 2. Moreover, the color gamut volume calculation method in the step 3 is a convex hull algorithm or an alpha-shape algorithm;
since the data measured in step 2 has high dimension, for the convenience of calculation, it can be first converted into a chromaticity space, and the embodiment adopts a CIELAB color space under the condition of D50/2. Subsequently, the respective media low density sample gamut volumes are calculated using a convex hull algorithm. The chrominance space conversion and the convex hull algorithm are respectively known in the fields of colorimetry and computer graphics, and specifically refer to the following steps:
Schanda J.CIE colorimetry:Wiley Online Library；2007.
Barber CB,Dobkin DP,Huhdanpaa H.The quickhull algorithm for convex hulls.ACM Transactions on Mathematical Software(TOMS).1996；22(4):469-83.
step 4, selecting an optimal color gamut medium J on the basis of a color gamut maximization principle, and constructing a high-density sample set G-based characterization model aiming at the J, wherein the high-density sample set G-based characterization model comprises a forward prediction model F and a reverse color separation model B, and in the step 4, the high-density sampling mode is space uniform sampling, and the number of sampling samples in each ink color dimension is not less than 5;
in the embodiment, as can be known from the calculation of the convex hull algorithm in step 3, in terms of the color gamut volume of the low-density sample T, H1> H2> H3, therefore, an H1 medium is adopted as an optimal medium J, a BPnCYNSN model, that is, a forward model F, is constructed for the optimal medium J, and then a sequential quadratic programming algorithm is used for performing mathematical inversion on the model F, so that the construction of a reverse color separation model B is realized. Here, the number of the sampling points of each ink color sampled at high spatial density is 6, which is 0,20,40,60,80,100, and 6 × 6 — 1296 ink color samples are generated (sample set G). Here, it is prior art to use the BPnCYNSN model and the sequential quadratic programming algorithm to respectively construct forward and backward models F and B, and see:
Liu Q,Wan X,Xie D.Optimization of spectral printer modeling based on a modified cellular Yule 2013；Nielsen spectral Neugebauer model.J Opt Soc Am A.2014；31(6):1284-94.
step 5, solving ink amount information T' corresponding to the color information S prepared in the step 1 by using the color model B in the step 4;
in the embodiment, taking H2 medium as an example, the spectral reflectance information S obtained by printing and measuring 256 samples in the sample set T on H2 medium is solved by using the inverse color separation model B constructed in step 4, so as to obtain 256 sets of ink amount information T'. For example, for the color spectral reflectance information corresponding to the ink samples (33,33,33,33) in T, after the color separation by the inverse color separation model B, the ink color values of the reflectance in the ink color space corresponding to the medium J are (28,36,30,19), i.e., the two sets of ink values have equivalence in the aspect of spectral reflectance characterization.
Step 6, constructing a neural network model between the color sample set T of other media samples and the sample set T' obtained by the color separation model B in the step 5 by using a neural network method;
subsequently, in the embodiment, 256 sets of four color ink values corresponding to T' are used as input, and 256 sets of four color models corresponding to T are used as output, so as to construct a three-layer BP neural network, wherein the construction method of the BP neural network is well known in the art, and the invention is not repeated.
And 7, aiming at any color information to be copied, firstly solving the ink amount information aiming at the J medium by using the color separation model B in the step 4, and then solving the ink amount information aiming at other media by using the neural network constructed in the step 6 to finish color separation.
In the embodiment, taking H2 medium as an example, if it is intended to copy the spectral reflectance information of a cyan sample from H2 medium, the color separation model B in step 4 can be used to solve the ink color information (80,11,3,24) corresponding to the optimal medium J, and the BP neural network constructed in step 6 is used to predict the ink color information (85,12,2,19) corresponding to H2 medium, so as to complete color separation.
In order to further prove the advantages of the method in the aspect of batch construction of the color separation models of the halftone equipment facing different media, the research adopts 100 randomly selected color samples to evaluate the accuracy progress of the color separation models of the H1, H2 and H3 media. The H1 medium is the medium J with the maximum color gamut, the average color separation precision is 1.02(CIELAB color difference formula, see Schanda J.CIE color algorithm: Wiley Online Library; 2007.) because of the high-density sampling modeling, and the color separation precision of the H2 and the H3 constructed based on the method is 2.46 and 2.29 respectively (CIELAB color difference formula). Although the accuracy of the color separation model is obviously lower than that of the high-density sampling modeling method compared with the high-density sampling modeling method, the accuracy of the color separation model constructed by the method is still in an acceptable range due to the fact that the acceptable color difference threshold value in the common color reproduction field is 3-6(CIELAB color difference formula). Compared with 1296 sampling samples in a high-density sampling mode, the method only needs 256 sampling samples, and is only 20% of the traditional method in the aspects of consumable cost and color measurement time. It is expected that in practical applications, the method of the present invention will be more advantageous when the number of types of media is significantly increased.
The invention provides a system for constructing color separation models of halftone equipment in batches facing different media, which comprises the following modules:
the low-density sampling module is used for uniformly sampling the ink color space of the equipment to obtain a low-density sample set T and preparing color samples aiming at each medium;
the color measuring module measures the color information of the color sample prepared by the low-density sampling module by using color measuring equipment;
the color gamut volume calculation module is used for calculating the color gamut volume of each medium color sample set by utilizing the color information obtained by the measurement of the color measurement module;
the optimal medium characterization module selects a color gamut optimal medium J on the principle of color gamut maximization, and constructs a characterization model based on a high-density sample set G aiming at the medium J, wherein the characterization model comprises a forward prediction model F and a reverse color separation model B;
the low-density sample color separation calculation module is used for solving ink amount information T' corresponding to the color information S prepared by the low-density sampling module from other medium sample sets T by using the color separation model B in the optimal medium characterization module;
the neural network construction module is used for constructing a neural network model between the other medium sample sets T and the sample set T' obtained by the color separation model B of the low-density sample color separation calculation module by using a neural network method;
and the final color separation module is used for solving the ink amount information aiming at the J medium by using a color separation model B in the optimal medium characterization module aiming at any color information to be copied, and then solving the ink amount information aiming at other media by using a neural network constructed by the neural network construction module to finish color separation.
The sampling mode of the low-density sample set T in the low-density sampling module is space uniform sampling, and the number of sampling samples in each ink dimension is 4.
The color information measured in the color measurement module can be chrominance information or spectral reflectivity information.
The color gamut volume calculating method in the color gamut volume calculating module is a convex hull algorithm or an alpha-shape algorithm.
The high-density sampling mode in the optimal medium characterization module is space uniform sampling, and the number of sampling samples in each ink dimension is not less than 5.
The specific implementation of each module corresponds to each step, and the detailed description of the invention is omitted.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (6)
1. A method for constructing color separation models of halftone equipment in batch facing different media is characterized by comprising the following steps:
step 1, uniformly sampling in an ink color space of equipment, wherein the number of sampling samples in each ink color dimension is 4, obtaining a low-density sample set T, and preparing a medium color sample T1 for each medium;
step 2, measuring the color information of the medium color sample T1 prepared in the step 1 by using color measuring equipment;
step 3, calculating the color gamut volume of the low-density sample set T by using the color information obtained by measurement in the step 2;
step 4, selecting an optimal color gamut medium J based on a color gamut maximization principle, uniformly sampling in a high-density space, wherein the number of sampling samples in each ink color dimension is not less than 5, and constructing a high-density sample set G-based characterization model aiming at J, wherein the characterization model comprises a forward prediction model F and a reverse color separation model B;
step 5, solving the ink amount information T' corresponding to the color information prepared in the step 2 by the low-density sample set T under other medium conditions by using the reverse color separation model B in the step 4;
step 6, constructing a neural network model between the low-density color sample set T and the ink amount information T' obtained by the reverse color separation model B in the step 5 under other medium conditions by using a neural network method;
and 7, aiming at any color information to be copied, firstly solving the ink amount information aiming at the J medium by using the reverse color separation model B in the step 4, and then solving the ink amount information aiming at other media by using the neural network model constructed in the step 6 to finish color separation.
2. The halftone apparatus color separation model batch construction method according to claim 1, characterized in that: and the color information measured in the step 2 is chrominance information or spectral reflectivity information.
3. The halftone apparatus color separation model batch construction method according to claim 1, characterized in that: and 3, the color gamut volume calculation method in the step 3 is a convex hull algorithm or an alpha-shape algorithm.
4. The system for constructing the color separation models of the halftone equipment in batch facing different media is characterized by comprising the following modules:
the low-density sampling module is used for uniformly sampling the ink color space of the device, the number of sampling samples in each ink color dimension is 4, a low-density sample set T is obtained, and a medium color sample T1 is prepared for each medium;
the color measuring module measures the color information of the medium color sample T1 prepared by the low-density sampling module by using color measuring equipment;
the color gamut volume calculation module is used for calculating the color gamut volume of the low-density sample set T by utilizing the color information obtained by the measurement of the color measurement module;
the optimal medium characterization module selects a color gamut optimal medium J based on a color gamut maximization principle, performs spatial uniform sampling, has no less than 5 sampling samples in each ink color dimension, and constructs a characterization model based on a high-density sample set G aiming at J, wherein the characterization model comprises a forward prediction model F and a reverse color separation model B;
the low-density sample color separation calculation module is used for solving the ink amount information T' corresponding to the color information prepared by the low-density sampling module in the low-density sample set T under other medium conditions by utilizing the reverse color separation model B in the optimal medium characterization module;
the neural network construction module is used for constructing a neural network model between the low-density sample set T and the ink quantity information T' obtained by the low-density sample color separation calculation module reverse color separation model B under other medium conditions by using a neural network method;
and the final color separation module is used for solving the ink amount information aiming at the J medium by using the color separation model B in the optimal medium characterization module aiming at any color information to be copied, and then solving the ink amount information aiming at other media by using the neural network model constructed by the neural network construction module to finish color separation.
5. The halftone apparatus color separation model batch building system according to claim 4, wherein: the color information measured in the color measurement module may be chrominance information or spectral reflectance information.
6. The halftone apparatus color separation model batch building system according to claim 4, wherein: the color gamut volume calculating method in the color gamut volume calculating module is a convex hull algorithm or an alpha-shape algorithm.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07212583A (en) * | 1994-01-14 | 1995-08-11 | Fuji Xerox Co Ltd | Method and device for processing color picture |
| CN106157336A (en) * | 2016-07-05 | 2016-11-23 | 臧戈平 | Figure numeral net hanging method and system is hidden based on the high-fidelity directly revised |
-
2017
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07212583A (en) * | 1994-01-14 | 1995-08-11 | Fuji Xerox Co Ltd | Method and device for processing color picture |
| CN106157336A (en) * | 2016-07-05 | 2016-11-23 | 臧戈平 | Figure numeral net hanging method and system is hidden based on the high-fidelity directly revised |
Non-Patent Citations (2)
| Title |
|---|
| Spectral-based color separation method for;王彬宇，徐海松，M. Ronnier Luo，郭晋一;《CHINESE OPTICS LETTERS》;20110610;全文 * |
| 基于光谱的颜色喷墨再现关键问题研究;刘强;《中国博士学位论文全文数据库》;20151215;全文 * |
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