A Relative Colorimetric Rending Mapping Method Based on BP Neural Network


Article Preview

There are very complicated nonlinear relations between input value and presentation color of digital prepress devices, as well as in the gamut mapping among different devices. Not all the relations can be described by linear model properly. In recent years, neural network and black box theory are introduced to describe the relations. In this research, the printer gamut is divided into micro space sections, a relative colorimetric rending mapping method is proposed based on BP neural network theory to complete the gamut mapping from monitor gamut to color printer. Experimental results show that the model reduces the color error of color blocks between the two devices, and the color rendering method can be used for gamut mapping of image with less continuous tone.



Edited by:

Qi Luo




L. Zhao and G. X. Chen, "A Relative Colorimetric Rending Mapping Method Based on BP Neural Network", Applied Mechanics and Materials, Vols. 55-57, pp. 1830-1833, 2011

Online since:

May 2011




[1] S.S. Zhou: Print Color Science (Printing Industry Press, China, 2005).

[2] M. Huang and D.Z. Zhao: A Color Printer Calibration Prediction Method. Journal of Beijing Institute of Technology Vol. 10 (2003), p.602.

[3] E.H. Zhang, X. Xue, Y. Wu, Z.G. Zhang: Research of Gamut Mapping Compression Methods of Color Management, Printed Journal (2003).

[4] D.Q. Zhu and H. Shi: Artificial Neural Network and Its Application (Science Press, China, 2006).

[5] K.L. Zhou and H.K. Yao: Matlab Neural Network Model and Its Simulation Program Design (Tsinghua University Press, China, 2005).

[6] S. Cong: Toolbox for MATLAB Neural Network Theory and Application (China Science and Technology University Press, China, 2008).

[7] C.R. Yuan: Artificial Neural Network and Its Application (Tsinghua University Press, China, 2009).

[8] S.Q. Zhou and D.Z. Zhao: Based on BP Neural Network Printer Color Control Technologies. Optical Technology Vol. 26 (2000), p.50.

[9] Fecit Research Center: Matlab7 Tutorials and Realization of Neural Networks (Tsinghua University Press, China, 2008).