No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement
by Jia Yan, Jie Li, Xin Fu
This repository contains the code for the paper "No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement", and presents the results on CCID2014，CID2013，CSIQ and TID2013 databases for image quality assessment.
Simply open MATLAB and run
To analyse the results in the paper, run
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.
Copyright © 2018, Jia Yan, Jie Li, Xin Fu
Released under the MIT License.