No-training, no-reference image quality index using perceptual features
Chaofeng Li, Yiwen Ju, Alan Conrad Bovik, Xiao‐Jun Wu, Qingbing Sang · Optical Engineering · 2013
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.