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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: 3496

Citations:  Bibtex RefMan EndNote GB/T7714

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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.

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journal="Journal of Zhejiang University Science C",
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%A Qiao-song Chen
%A Choon-woo Kim
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%I Zhejiang University Press & Springer
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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


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