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Optical Engineering

No-training, no-reference image quality index using perceptual features
Author(s): Chaofeng Li; Yiwen Ju; Alan C. Bovik; Xiaojun Wu; Qingbing Sang
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Paper Abstract

We propose a universal no-reference (NR) image quality assessment (QA) index that does not require training on human opinion scores. The new index utilizes perceptually relevant image features extracted from the distorted image. These include the mean phase congruency (PC) of the image, the entropy of the phase congruencyPC image, the entropy of the distorted image, and the mean gradient magnitude of the distorted image. Image quality prediction is accomplished by using a simple functional relationship of these features. The experimental results show that the new index accords closely with human subjective judgments of diverse distorted images.

Paper Details

Date Published: 7 May 2013
PDF: 7 pages
Opt. Eng. 52(5) 057003 doi: 10.1117/1.OE.52.5.057003
Published in: Optical Engineering Volume 52, Issue 5
Show Author Affiliations
Chaofeng Li, Jiangnan Univ. (China)
Yiwen Ju, Univ. of the Chinese Academy of Sciences (China)
Alan C. Bovik, The Univ. of Texas at Austin (United States)
Xiaojun Wu, Jiangnan Univ. (China)
Qingbing Sang, Jiangnan Univ. (China)


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