No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement
This addresses a specific gap in image quality assessment for contrast-distorted images, but it is incremental as it builds on existing methods like SSIM and histogram analysis.
The paper tackles the problem of no-reference quality assessment for images with contrast distortion by proposing a simple metric based on similarity between an image and its contrast-enhanced version, achieving validated superiority and efficiency on four public databases.
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.