A Method of Obtaining Gray Balance Data Based on Polynomial Regression

Bang Yong Sun; Shi Sheng Zhou; Cong Jun Cao; Yuan Lin Zheng

doi:10.4028/www.scientific.net/AMR.213.643

Paper Titles

Experimental Research on the Normal Absorption Coefficient of the Multilayer Material
p.608

The Optimized Design for Groove Parameters Based on Cement Pavement Materials Performance
p.613

Industrial Diversification and Regional Development Stability: Theoretical Discussion
p.618

Flow Stress Constitutive Equation of Age Hardening Cu-1.1Cr Alloy during Hot Compression Deformation
p.623

Computer-Aided Design and Fabrication of Bio-Mimetic Materials and Scaffold Micro-Structures
p.628

Study on Bonding Performance of Interlayer Materials between Old Cement Slab and Asphalt Overlay
p.633

A Multi-Dimension Configuration Method of Modular Machine Tools Based on Customer Requirements-Driven
p.638

A Method of Obtaining Gray Balance Data Based on Polynomial Regression
p.643

Design and Implementation of Boot Loader Based on Embedded System Platform
p.647

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 213A Method of Obtaining Gray Balance Data Based on...

A Method of Obtaining Gray Balance Data Based on Polynomial Regression

Abstract:

As gray balance is very important for color separation and printing quality control, a method based on polynomial regression is proposed to determining the gray balance data in printing process. Firstly the patches on GrayFinder target of GRACoL proof are measured and the L*a*b* values are obtained, then the relationship between L*a*b* and CMY is determined by using the polynomial regression method, at last the different lightness’ corresponding CMY values are found which constitute the gray balance data by using the relationship obtained above. In the experiment, 8 gray patches are used to test the algorithm, and the result shows the average error is 0.969 with the maximal error 2.047 which indicates that the method can be used to find gray balance data.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 213)

Pages:

643-646

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.213.643

Citation:

Cite this paper

Online since:

February 2011

Authors:

Bang Yong Sun, Shi Sheng Zhou, Cong Jun Cao, Yuan Lin Zheng

Keywords:

Gray Balance, GrayFinder Target, Polynomial Regression

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Zeng H Z. Gray Component Replacement by Direct Colorimetric Mapping. Device-Independent Color, Color Hardcopy and Graphic Arts, SPIE Conference on Color Imaging. California: SPIE, 2000: 317-322.

DOI: 10.1117/12.373411

Google Scholar

[2] Li Wen-yu, Zhou Shi-sheng. The Calculation of Gray Balance, Packaging Engineering, 2004, 25(1): 65-67.

Google Scholar

[3] G. Pawle, L. Borg. Evolution of the ICC Profile Connection Space, Proceedings of SPIE, SPIE Press, 2002, 4421: 446-450.

Google Scholar

[4] H R Kang, G Anderson. Neural network application to color scanner and printer calibrations. Electron Image, 1992, 1(2): 125-135.

Google Scholar

[5] Bong sun Lee, Raja Bala, Gaurav Sharma. Novel scanner characterization method for color measurement and diagnostics applications. Proc. SPIE-IS&T, 2006, 6065: 1-12.

Google Scholar

[6] H R Kang. Color technology for electronic imaging devices, SPIE Optical Engineering Press, Bellingham, Washington, (1997).

Google Scholar

[7] Hong G W, Luo M R, Rhodes P A. A study of digital camera colorimetric characterization based on polynomial modeling. Color Research and Application, 2001, 26(1): 76-84.

DOI: 10.1002/1520-6378(200102)26:1<76::aid-col8>3.0.co;2-3

Google Scholar