No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement

TL;DR

This paper introduces a simple yet effective no-reference image quality assessment method for contrast-distorted images, leveraging contrast enhancement and multiple similarity features to accurately predict image quality.

Contribution

It proposes a novel NR-IQA metric based on contrast enhancement and feature fusion, addressing the overlooked issue of contrast distortion in image quality assessment.

Findings

Outperforms existing NR-IQA methods on four public databases.

Efficient and effective in predicting contrast distortion quality.

Uses a combination of SSIM, entropy, and cross entropy features.

Abstract

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.