A technical review of no-reference image quality assessment algorithms for contrast distorted images

Abstract

Automatic image quality assessment similar to human vision perception is an essential process for real time image processing applications to perform perceptual image assessments for effectively achieving its goals. As no-reference image quality assessment (NR-IQA) schemes perform perceptual assessments of images without any information about original images, these algorithms suit real time computer vision techniques because of the non-availability of reference images. Contrast and colorfulness play important roles in determining the quality of color images. By combining many IQA metrics, a number of combined metrics had been devised.  This study provides an insight of major NR-IQA methods and their effectiveness in assessing contrast, colorfulness and overall quality of contrast-degraded images with technical analysis.  The effectiveness of the top-ranking NR-IQA methods are experimentally assessed with benchmark assessment methods on images from benchmarked databases. The study provides the insight of open research challenges in the area of NR-IQA for developing new promising methods by clearly demarcating the difficulties of top-ranking NR-IQA methods.