  No-reference image quality assessment (NR-IQA) aims to measure the image
quality without reference image. However, contrast distortion has been
overlooked in the current research of NR-IQA. In this paper, we propose a very
simple but effective metric for predicting quality of contrast-altered images
based on the fact that a high-contrast image is often more similar to its
contrast enhanced image. Specifically, we first generate an enhanced image
through histogram equalization. We then calculate the similarity of the
original image and the enhanced one by using structural-similarity index (SSIM)
as the first feature. Further, we calculate the histogram based entropy and
cross entropy between the original image and the enhanced one respectively, to
gain a sum of 4 features. Finally, we learn a regression module to fuse the
aforementioned 5 features for inferring the quality score. Experiments on four
publicly available databases validate the superiority and efficiency of the
proposed technique.
