桌面喷墨打印机的颜色特性化研究
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摘要
桌面喷墨打印机是日常生活中重要的颜色再现设备,其颜色特性化包括两个主要部分：打印机的前向特性化,即建立与设备相关的RGB颜色空间到与设备无关的光谱或色度之间的转换；打印机的逆向特性化,即建立已知的反射光谱到RGB颜色空间的转换,以尽可能准确的再现颜色的光谱或色度。相对来说,逆向模型对于实际应用更有价值。
论文首先根据物理混色原理建立前向打印模型,分析了打印机内的墨水分色过程和介质上的呈色过程。对于呈色过程,分别采用基于简单点增益的YNSN模型和基于复杂点增益的改进型YNSN模型。并通过非线性优化求解分色过程中图像RGB对应的CMYK喷墨量,并建立两者间的多项式回归关系。论文还采用三维查找表法和局部多项式回归法实现打印机的前向颜色特性化。最后提出了一种六邻域局部空间算法,通过权重参数和幂因子的优化,进一步提高了算法精度。
在研究逆向打印模型时,首先采用单光源多项式回归法建立CIEXYZ到RGB空间的映射关系,实现了在本光源下的精确颜色再现,但其在不同光源下未能获得较好效果。为此,本论文最后提出了一种局部空间数值优化方法,通过参数的优化,进一步提高了逆向模型的算法精度,实现了多光谱图像的高保真打印。
For a desktop color printer, which is a typical output device, the characterization consists of forward and inverse models. The forward model seeks to convert the device-dependent control values (e.g., RGB) to the device-independent reflectance spectrum or colorimetric stimulus. The inverse model, which is more useful in color applications, transforms from a requested reflectance spectrum to the optimal control values that, when printed, could reproduce that spectrum or colorimetric stimulus with greatest possible accuracy.
First of all, establish the forward model, based on the principle of physical mixed color. The method investigates the printer color separation and medium color rendering processes, based on which, spectral reflectance can be predicted. The YNSN and enhanced YNSN models, which are based on simple and complicated dot gain respectively, are employed in the printer color separation process. As the color separation cannot be directly controlled, the colorimetric error objective function is designed. The mapping between RGB and CMYK is modeled using polynomial regression, and is resolved by nonlinear optimization. In the paper, we also use 3D-LUT and local polynomial regression to establish the forward model. Finally, six field local space algorithm is presented. Through the weight parameters and the optimization of power factor, the algorithm precision is improved.
For the inverse model, first establish the mapping between CIEXYZ and RGB with polynomial regression, which is under a single light. The algorithm has good colorimetric accuracy under the light and leads to bad results in a different light. To solve the problem, the method of RGB averaging is presented. Finally, a local space numerical optimization method is presented. Through the optimization of parameters, the algorithm precision is improved and the good accuracy of the print of multispectral image is realized.
论文首先根据物理混色原理建立前向打印模型,分析了打印机内的墨水分色过程和介质上的呈色过程。对于呈色过程,分别采用基于简单点增益的YNSN模型和基于复杂点增益的改进型YNSN模型。并通过非线性优化求解分色过程中图像RGB对应的CMYK喷墨量,并建立两者间的多项式回归关系。论文还采用三维查找表法和局部多项式回归法实现打印机的前向颜色特性化。最后提出了一种六邻域局部空间算法,通过权重参数和幂因子的优化,进一步提高了算法精度。
在研究逆向打印模型时,首先采用单光源多项式回归法建立CIEXYZ到RGB空间的映射关系,实现了在本光源下的精确颜色再现,但其在不同光源下未能获得较好效果。为此,本论文最后提出了一种局部空间数值优化方法,通过参数的优化,进一步提高了逆向模型的算法精度,实现了多光谱图像的高保真打印。
For a desktop color printer, which is a typical output device, the characterization consists of forward and inverse models. The forward model seeks to convert the device-dependent control values (e.g., RGB) to the device-independent reflectance spectrum or colorimetric stimulus. The inverse model, which is more useful in color applications, transforms from a requested reflectance spectrum to the optimal control values that, when printed, could reproduce that spectrum or colorimetric stimulus with greatest possible accuracy.
First of all, establish the forward model, based on the principle of physical mixed color. The method investigates the printer color separation and medium color rendering processes, based on which, spectral reflectance can be predicted. The YNSN and enhanced YNSN models, which are based on simple and complicated dot gain respectively, are employed in the printer color separation process. As the color separation cannot be directly controlled, the colorimetric error objective function is designed. The mapping between RGB and CMYK is modeled using polynomial regression, and is resolved by nonlinear optimization. In the paper, we also use 3D-LUT and local polynomial regression to establish the forward model. Finally, six field local space algorithm is presented. Through the weight parameters and the optimization of power factor, the algorithm precision is improved.
For the inverse model, first establish the mapping between CIEXYZ and RGB with polynomial regression, which is under a single light. The algorithm has good colorimetric accuracy under the light and leads to bad results in a different light. To solve the problem, the method of RGB averaging is presented. Finally, a local space numerical optimization method is presented. Through the optimization of parameters, the algorithm precision is improved and the good accuracy of the print of multispectral image is realized.
引文
[1]D. R. Wyble, R.S. Berns. A Critical Review of Spectral Models Applied to Binary Color Printing [J]. Color Research and Application,2000,25(1):4-29.
[2]J.A.S Viggiano.Modeling the Color of Multi-Colored Halftones [J]. Proc. TAGA. 1990:44-62.
[3]N. Fdhal, M. Kyan, D. Androutsos et al.. Color Space Transformation from RGB to CIELAB Using Neural Networks [C]. Lecture Notes in Computer Science,2009, 5879:1011-1017.
[4]成刚虎,熊康鹏.纽介堡方程的理论价值及局限性研究[J].中国印刷与包装研究,2010,02(5)：24-28.
[5]J.A.C. Yule, W.J. Nielsen. The penetration of light into paper and its effect on halftone reproductions [J], Proc. TAGA,1951(3):65-76.
[6]P. Urban and M.R. Rosen, R.S. Berns. Accelerating spectral-based color separation within the Neugebauer subspace [J], Journal Electronic Imaging,2007,16(4):1-11.
[7]M. Hebert, R.D. Hersch. Yule-Nielsen based recto-verso color halftone transmittance prediction model [C]. IS&T/SID Conference on Color Imaging,2009,155-158.
[8]L. A. Taplin, R.S. Berns. Spectral color reproduction based on a six-color inkjet output system [C]. IS&T/SID Conference on Color Imaging,2001,209-213.
[9]Guo Jinyi, Xu Haisong, M. Ronnier Luo. Novel spectral characterization method for color printer based on the cellular Neugebauer model [J]. Chinese Optics Letters, 2010,8(11):1106-1109.
[10]S. Zuffi, R. Schettini, G. Mauri. Spectral-based printer modeling and characterization [J]. Journal of Electronic Imaging,2005,14(2):1-9.
[11]F.R.Clapper and J.A.C Yule, the effect of multiple internal reflections on the densities of half-tone prints on paper [J]. Optical Soc.Amr,1953(43):600-603.
[12]P.Kubelka. New contributions to optics of intensely light-scattering materials Part Ⅰ [J]. Optical Soc.Amr,1948(38):448-457.
[13]I. Jang, C. Son, T. Park et al.. Hi-fi printer characterization method Using color correlation for gamut extension [C]. IEEE International Conference on Image Processing,2006,1517-1520.
[14]Deng Jiabin, Hu Juanli, Wu Juebo. A Study of Color Space Transformation Method Using Nonuniform Segmentation of Color Space Source [J]. Journal of Computers, 2011,6(2):288-296.
[15]J. Morovic, J. Arnabat, Y. Richard, and A. Albarran. Sampling optimization for printer characterization by greedy search [J]. IEEE Transactions Image Processing.2010(19):2705-2711.
[16]D. Littlewood. G. Subbarayan. Maintaining an accurate printer characterization[C]. IS&T/SID Conference on Color Imaging.2004.203-210.
[17]M. Xia, E. Saber, G. Sharma et al.. End-to-end color printer calibration by total least squares regression [C], IEEE Transactions on Image Processing,1999,8(5):700-716.
[18]J. Morovic and M.R. Luo. Characterising desktop colour printers without full control over all colorants [C]. The Fourth Color Imaging Conference:Color Science, System, and Applications (IS&T.1996),70-74.
[19]M. S. Drew, G. D. Finlayson. M. R. Luo, and J. Morovic. Colour printer characterization by regression with grayspace constraint [C]. The Sixth Color Imaging Conference:Color Science, System, and Applications (IS&T,1998), 193-196.
[20]郭茂祖,王亚东,苏晓红等.基于BP网络的色彩匹配方法研究[J].计算机学报,2000,23(8)：819-823.
[21]N. Fdhal, M. Kyan, D. Androutsos et al.. Color Space Transformation from RGB to CIELAB Using Neural Networks [C]. Lecture Notes in Computer Science,2009, 5879:1011-1017.
[22]曹从军,孙静.基于广义回归神经网络的数码打样色彩空间转换方法[J].计算机应用,2010,30(8)：2108-2110.
[23]A. Verikas, K. Malmqvist and L.Bergman. Neural networks based colour measuring for process monitoring and control in multicolored newspaper printing [J], Neural Computing and Applications 9.
[24]单冬红,赵伟艇.基于主成分分析的神经网络入侵检测仿真研究[J].计算机仿真,2011,28(6)：153-156.
[25]A. Jaramillo, K. Yamaba. An Artificial Neural Network for Classification of Color Images [C]. Proc. IS&T/SID's 2nd Color Imaging Conference,1993:167-173.
[26]R. Bala and G. Sharma. System optimization in digital color imaging [J], IEEE Signal Processing Magazine.2005(14):55-63.
[27]I. S. Jang, C. H. Son, T. Y. Park, and Y H. Ha. Improved inverse characterization of multicolorant printer using colorant correlation [J]. Journal of Imaging Science and Technology,2007(51):175-184.
[28]徐海松.颜色信息工程[D].杭州：浙江大学出版社,2005：22.
[29]R.G.Kuehni, Color-tolerance data and the tentative CIE1976Lab formula [J]. Opt. Soc. Am,1976(66):498-499.
[30]池田光男,视觉的心理物理学,森被出版株式会社,1975.
[31]F. J. J. Clarke, R. McDonald, and B. Rigg. Modification to the JPC79 color difference formula [J]. Soc. Dyers Colour,1984(100):128-132.
[32]Industrial Color-Difference Evaluation. CIE Publ.116.CIE Central Bureau.1995.
[33]M. R. Luo, G. Cui, and B. Rigg. The development of the CIE 2000 colour difference formula:CIEDE2000 [J]. Color Research and Application.2001(26):340-350.
[34]Improvement to Industrial Colour-Difference Evaluation,CIE Publ.2001-142.
[35]Zhang Xiandou, Xu Haisong. An adaptively spatial color gamut mapping algorithm [J]. Chinese Optics Letters,2009,7(9):873-877.
[36]R. D. Hersch, P. Emmel, F. Collaud et al.. Spectral reflection and dot surface prediction models for color halftone printers [J]. Journal of Electronic Imaging,2005, 14(3):33001-1-12.
[37]R. D. Hersch, F. Crete. Improving the Yule-Nielsen modified spectral Neugebauer model by dot surface coverages depending on the ink superposition conditions [C]. IS&T/SPIE Conference on Electronic Imaging Symposium,2005,5667:434-445.
[38]王莹,曾平,王义峰等.一种面向多光谱图像的打印分色方法[J].光学学报,2009,29(8)：2122～2127.
[39]苏小红,张田文,王亚东等.四色打印的灰度平衡方法研究[J].计算机研究与发展,2002.39(12)：1695-1702.
[40]N. Shimano. Recovery of spectral reflectance of objects being imaged without prior knowledge [J].IEEE Transactions on Image Processing,2006(15):1848-1856.
[41]J. Brauers and T. Aach. Geometric calibration of lens and filter distortions for multispectral filter-wheel cameras [J]. IEEE Transactions on Image Processing.2011 (20):496-505.
[42]H. L. Shen, C. W. Weng, H. J. Wan, and J. H. Xin. Correcting cross-media instrument metamerism for reflectance estimation in multispectral imaging [J]. Journal of the Optical Society of America A,2011(28):511-516.
[43]R. Storn and K. V. Price. Differential evolution -a simple and efficient heuristic for global optimization over continuous space [J]. Journal of Global Optimization,1997 (11):341-359.
[44]R. Storn, K. V. Price, and J. Lampinen. Differential Evolution -A Practical Approach to Global Optimization (Berlin, Germany:Springer-Verlag,2005).
[45]R. Joshi and A. C. Sanderson. Minimum representation multi-sensor fusion using differential evolution [J]. IEEE Transactions on System, Man, and Cybernetics, Part A,1999(29):63-76.
[46]S. Das, A. Abraham, and A. Konar. Adaptive clustering using improved differential evolution algorithm [J]. IEEE Transactions on System, Man, and Cybernetics, Part A, 2008(38):218-237.
[47]S. Das. A. Abraham. U. K. Chakraborty, and A. Konar. Differential evolution using a neighborhood-based mutation operator [J]. IEEE Transactions on Evolutionary Computation,2009(13):526-553.
[48]A. K. Qin, V. L. Huang, and P. N. Suganthan. Differential evolution algorithm with strategy adaptation for global numerical optimzation [J]. IEEE Transactions on Evolutionary Computation,2009(13):398-417.
[49]M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta. A standard default color space for the internet:sRGB. Version 1.10, ICC (1996).
[2]J.A.S Viggiano.Modeling the Color of Multi-Colored Halftones [J]. Proc. TAGA. 1990:44-62.
[3]N. Fdhal, M. Kyan, D. Androutsos et al.. Color Space Transformation from RGB to CIELAB Using Neural Networks [C]. Lecture Notes in Computer Science,2009, 5879:1011-1017.
[4]成刚虎,熊康鹏.纽介堡方程的理论价值及局限性研究[J].中国印刷与包装研究,2010,02(5)：24-28.
[5]J.A.C. Yule, W.J. Nielsen. The penetration of light into paper and its effect on halftone reproductions [J], Proc. TAGA,1951(3):65-76.
[6]P. Urban and M.R. Rosen, R.S. Berns. Accelerating spectral-based color separation within the Neugebauer subspace [J], Journal Electronic Imaging,2007,16(4):1-11.
[7]M. Hebert, R.D. Hersch. Yule-Nielsen based recto-verso color halftone transmittance prediction model [C]. IS&T/SID Conference on Color Imaging,2009,155-158.
[8]L. A. Taplin, R.S. Berns. Spectral color reproduction based on a six-color inkjet output system [C]. IS&T/SID Conference on Color Imaging,2001,209-213.
[9]Guo Jinyi, Xu Haisong, M. Ronnier Luo. Novel spectral characterization method for color printer based on the cellular Neugebauer model [J]. Chinese Optics Letters, 2010,8(11):1106-1109.
[10]S. Zuffi, R. Schettini, G. Mauri. Spectral-based printer modeling and characterization [J]. Journal of Electronic Imaging,2005,14(2):1-9.
[11]F.R.Clapper and J.A.C Yule, the effect of multiple internal reflections on the densities of half-tone prints on paper [J]. Optical Soc.Amr,1953(43):600-603.
[12]P.Kubelka. New contributions to optics of intensely light-scattering materials Part Ⅰ [J]. Optical Soc.Amr,1948(38):448-457.
[13]I. Jang, C. Son, T. Park et al.. Hi-fi printer characterization method Using color correlation for gamut extension [C]. IEEE International Conference on Image Processing,2006,1517-1520.
[14]Deng Jiabin, Hu Juanli, Wu Juebo. A Study of Color Space Transformation Method Using Nonuniform Segmentation of Color Space Source [J]. Journal of Computers, 2011,6(2):288-296.
[15]J. Morovic, J. Arnabat, Y. Richard, and A. Albarran. Sampling optimization for printer characterization by greedy search [J]. IEEE Transactions Image Processing.2010(19):2705-2711.
[16]D. Littlewood. G. Subbarayan. Maintaining an accurate printer characterization[C]. IS&T/SID Conference on Color Imaging.2004.203-210.
[17]M. Xia, E. Saber, G. Sharma et al.. End-to-end color printer calibration by total least squares regression [C], IEEE Transactions on Image Processing,1999,8(5):700-716.
[18]J. Morovic and M.R. Luo. Characterising desktop colour printers without full control over all colorants [C]. The Fourth Color Imaging Conference:Color Science, System, and Applications (IS&T.1996),70-74.
[19]M. S. Drew, G. D. Finlayson. M. R. Luo, and J. Morovic. Colour printer characterization by regression with grayspace constraint [C]. The Sixth Color Imaging Conference:Color Science, System, and Applications (IS&T,1998), 193-196.
[20]郭茂祖,王亚东,苏晓红等.基于BP网络的色彩匹配方法研究[J].计算机学报,2000,23(8)：819-823.
[21]N. Fdhal, M. Kyan, D. Androutsos et al.. Color Space Transformation from RGB to CIELAB Using Neural Networks [C]. Lecture Notes in Computer Science,2009, 5879:1011-1017.
[22]曹从军,孙静.基于广义回归神经网络的数码打样色彩空间转换方法[J].计算机应用,2010,30(8)：2108-2110.
[23]A. Verikas, K. Malmqvist and L.Bergman. Neural networks based colour measuring for process monitoring and control in multicolored newspaper printing [J], Neural Computing and Applications 9.
[24]单冬红,赵伟艇.基于主成分分析的神经网络入侵检测仿真研究[J].计算机仿真,2011,28(6)：153-156.
[25]A. Jaramillo, K. Yamaba. An Artificial Neural Network for Classification of Color Images [C]. Proc. IS&T/SID's 2nd Color Imaging Conference,1993:167-173.
[26]R. Bala and G. Sharma. System optimization in digital color imaging [J], IEEE Signal Processing Magazine.2005(14):55-63.
[27]I. S. Jang, C. H. Son, T. Y. Park, and Y H. Ha. Improved inverse characterization of multicolorant printer using colorant correlation [J]. Journal of Imaging Science and Technology,2007(51):175-184.
[28]徐海松.颜色信息工程[D].杭州：浙江大学出版社,2005：22.
[29]R.G.Kuehni, Color-tolerance data and the tentative CIE1976Lab formula [J]. Opt. Soc. Am,1976(66):498-499.
[30]池田光男,视觉的心理物理学,森被出版株式会社,1975.
[31]F. J. J. Clarke, R. McDonald, and B. Rigg. Modification to the JPC79 color difference formula [J]. Soc. Dyers Colour,1984(100):128-132.
[32]Industrial Color-Difference Evaluation. CIE Publ.116.CIE Central Bureau.1995.
[33]M. R. Luo, G. Cui, and B. Rigg. The development of the CIE 2000 colour difference formula:CIEDE2000 [J]. Color Research and Application.2001(26):340-350.
[34]Improvement to Industrial Colour-Difference Evaluation,CIE Publ.2001-142.
[35]Zhang Xiandou, Xu Haisong. An adaptively spatial color gamut mapping algorithm [J]. Chinese Optics Letters,2009,7(9):873-877.
[36]R. D. Hersch, P. Emmel, F. Collaud et al.. Spectral reflection and dot surface prediction models for color halftone printers [J]. Journal of Electronic Imaging,2005, 14(3):33001-1-12.
[37]R. D. Hersch, F. Crete. Improving the Yule-Nielsen modified spectral Neugebauer model by dot surface coverages depending on the ink superposition conditions [C]. IS&T/SPIE Conference on Electronic Imaging Symposium,2005,5667:434-445.
[38]王莹,曾平,王义峰等.一种面向多光谱图像的打印分色方法[J].光学学报,2009,29(8)：2122～2127.
[39]苏小红,张田文,王亚东等.四色打印的灰度平衡方法研究[J].计算机研究与发展,2002.39(12)：1695-1702.
[40]N. Shimano. Recovery of spectral reflectance of objects being imaged without prior knowledge [J].IEEE Transactions on Image Processing,2006(15):1848-1856.
[41]J. Brauers and T. Aach. Geometric calibration of lens and filter distortions for multispectral filter-wheel cameras [J]. IEEE Transactions on Image Processing.2011 (20):496-505.
[42]H. L. Shen, C. W. Weng, H. J. Wan, and J. H. Xin. Correcting cross-media instrument metamerism for reflectance estimation in multispectral imaging [J]. Journal of the Optical Society of America A,2011(28):511-516.
[43]R. Storn and K. V. Price. Differential evolution -a simple and efficient heuristic for global optimization over continuous space [J]. Journal of Global Optimization,1997 (11):341-359.
[44]R. Storn, K. V. Price, and J. Lampinen. Differential Evolution -A Practical Approach to Global Optimization (Berlin, Germany:Springer-Verlag,2005).
[45]R. Joshi and A. C. Sanderson. Minimum representation multi-sensor fusion using differential evolution [J]. IEEE Transactions on System, Man, and Cybernetics, Part A,1999(29):63-76.
[46]S. Das, A. Abraham, and A. Konar. Adaptive clustering using improved differential evolution algorithm [J]. IEEE Transactions on System, Man, and Cybernetics, Part A, 2008(38):218-237.
[47]S. Das. A. Abraham. U. K. Chakraborty, and A. Konar. Differential evolution using a neighborhood-based mutation operator [J]. IEEE Transactions on Evolutionary Computation,2009(13):526-553.
[48]A. K. Qin, V. L. Huang, and P. N. Suganthan. Differential evolution algorithm with strategy adaptation for global numerical optimzation [J]. IEEE Transactions on Evolutionary Computation,2009(13):398-417.
[49]M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta. A standard default color space for the internet:sRGB. Version 1.10, ICC (1996).