No-training, no-reference image quality index using perceptual features

Chaofeng Li; Yiwen Ju; Alan C. Bovik; Xiaojun Wu; Qingbing Sang

doi:10.1117/1.OE.52.5.057003

7 May 2013 No-training, no-reference image quality index using perceptual features

Chaofeng Li, Yiwen Ju, Alan C. Bovik, Xiaojun Wu, Qingbing Sang

Author Affiliations +

Optical Engineering, Vol. 52, Issue 5, 057003 (May 2013). https://doi.org/10.1117/1.OE.52.5.057003

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.

Citation Download Citation

Chaofeng Li, Yiwen Ju, Alan C. Bovik, Xiaojun Wu, and Qingbing Sang "No-training, no-reference image quality index using perceptual features," Optical Engineering 52(5), 057003 (7 May 2013). https://doi.org/10.1117/1.OE.52.5.057003

Published: 7 May 2013

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