Full Text:   <3588>

CLC number: TP317.4

On-line Access: 2011-11-04

Received: 2011-01-02

Revision Accepted: 2011-06-09

Crosschecked: 2011-09-28

Cited: 2

Clicked: 7211

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.11 P.897-909


Contrast evaluation methods for natural color images in display systems: within- and cross-content evaluations

Author(s):  Qiao-song Chen, Choon-woo Kim

Affiliation(s):  School of Information and Communication Engineering, Inha University, Incheon 402-751, Korea

Corresponding email(s):   cwkim@inha.ac.kr

Key Words:  Contrast evaluation, Global contrast, Local contrast, Within content, Cross content, Statistical significance

Qiao-song Chen, Choon-woo Kim. Contrast evaluation methods for natural color images in display systems: within- and cross-content evaluations[J]. Journal of Zhejiang University Science C, 2011, 12(11): 897-909.

@article{title="Contrast evaluation methods for natural color images in display systems: within- and cross-content evaluations",
author="Qiao-song Chen, Choon-woo Kim",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Contrast evaluation methods for natural color images in display systems: within- and cross-content evaluations
%A Qiao-song Chen
%A Choon-woo Kim
%J Journal of Zhejiang University SCIENCE C
%V 12
%N 11
%P 897-909
%@ 1869-1951
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100004

T1 - Contrast evaluation methods for natural color images in display systems: within- and cross-content evaluations
A1 - Qiao-song Chen
A1 - Choon-woo Kim
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 11
SP - 897
EP - 909
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1100004

contrast evaluation can be used as a criterion to evaluate performance of contrast enhancement algorithms and to compare contrast capability of display systems. This paper deals with contrast evaluation models for natural color images. Two separate models are defined for within- and cross-content evaluations. The former is to differentiate the perceived contrast of the images with the same content. The latter is to discriminate the differences in contrast among the images with different contents. Perception mechanisms are quite different for within- and cross-content evaluations. local contrast plays an important role in within-content evaluation. In contrast, global contrast dominates the contrast perception for cross-content evaluation. Results of human visual experiments show that the proposed evaluation models outperform previous methods for both within- and cross-content evaluations.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Agaian, S., Silver, B., Panetta, K., 2007. Transform coefficient histogram based image enhancement algorithms using contrast entropy. IEEE Trans. Image Process., 16(3):741-758.

[2]Bartleson, C.J., 1984. Optical Radiation Measurements. Academic Press, New York.

[3]Calabria, A.J., Fairchild, M.D., 2003a. Perceived image contrast and observer preference I. The effects of lightness, chroma, and sharpness manipulations on contrast perception. J. Imag. Sci. Technol., 47(6):479-493.

[4]Calabria, A.J., Fairchild, M.D., 2003b. Perceived image contrast and observer preference II. Empirical modeling of perceived image contrast and observer preference data. J. Imag. Sci. Technol., 47(6):494-508.

[5]Chen, Z.Y., Abidi, B.R., Page, D.L., Abidi, M.A., 2006. Gray-Level Grouping (GLG): an automatic method for optimized image contrast enhancement - part I: the basic method. IEEE Trans. Image Process., 15(8):2290-2302.

[6]Engeldrum, P.G., 2000. Psychometric Scaling: a Toolkit for Image Systems Development. Imcotek Press, Winchester.

[7]Fairchild, M.D., 2005. Color Appearance Models. John Wiley and Sons, Ltd., West Sussex.

[8]Fisher, R.A., 1925. Statistical Methods for Research Workers. Oliver and Boyd Ltd., London.

[9]Gescheider, G.A., 1984. Psychophysics: Method, Theory, and Application. Lawrence Erlbaum, Hillsdale.

[10]ISO 20462-1:2005. Photography - Psychophysical Experimental Methods for Estimating Image Quality - Part 1: Overview of Psychophysical Elements. International Organization for Standardization, Geneva.

[11]Keelan, B.W., 2002. Handbook of Image Quality: Characterization and Prediction. CRC Press, New York.

[12]Kim, S.Y., Han, D., Choi, S.J., Park, J.S., 1999. Image contrast enhancement based on the piecewise-linear approximation of CDF. IEEE Trans. Consum. Electron., 45(3):828-834.

[13]Krotkov, E.P., 1989. Active Computer Vision by Cooperative Focus and Stereo. Springer-Verlag, New York.

[14]Matkovic, K., Neumann, L., Neumann, A., Psik, T., Purgathofer, W., 2005. Global Contrast Factor—a New Approach to Image Contrast. Computational Aesthetics in Graphics, Visualization and Imaging, p.159-168.

[15]Michelson, A.A., 1927. Studies in Optics. University of Chicago Press, Chicago.

[16]Panetta, K., Wharton, E., Agaian, S., 2008. Human visual system-based image enhancement and logarithmic contrast measure. IEEE Trans. Syst. Man Cybern. Part B, 38(1):174-188.

[17]Pedersen, M., Rizzi, A., Hardeberg, J.Y., Simone, G., 2008. Evaluation of Contrast Measures in Relation to Observers Perceived Contrast. 4th European Conf. on Color in Graphics, Imaging, and Vision and 10th Int. Symp. on Multispectral Color Science, p.253-258.

[18]Peli, E., 1990. Contrast in complex image. J. Opt. Soc. Am. A, 7(10):2032-2040.

[19]Thurstone, L.L., 1927. A law of comparative judgment. Psychol. Rev., 34(4):273-286.

[20]Weber, E.H., 1846. Tastsinn und das Gemeingefuhl. Wagner’s Handworterbuch der Physiologie, Braunschweig (in German).

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE