Open AccessA technical review of no-reference image quality assessment algorithms for contrast distorted images
Author(s) -
Preeti Mittal,
Rajesh K. Saini,
Justin Varghese,
Neeraj Jain
Publication year - 2021
Publication title -
maǧallaẗ al-abḥāṯ al-handasiyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2307-1885
pISSN - 2307-1877
DOI - 10.36909/jer.11885
Subject(s) - computer science , contrast (vision) , image quality , ranking (information retrieval) , artificial intelligence , benchmark (surveying) , image processing , quality (philosophy) , computer vision , process (computing) , quality score , perception , image (mathematics) , pattern recognition (psychology) , metric (unit) , philosophy , operations management , geodesy , epistemology , neuroscience , economics , biology , geography , operating system
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 their goals. As no-reference image quality assessment (NR-IQA) schemes perform perceptual assessments of images without any information about their original version, 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 into major NR-IQA methods and their effectiveness in assessing contrast, colorfulness, and overall quality of contrast-degraded images with technical analysis. The effectiveness of top-ranking NR-IQA methods is experimentally assessed with benchmark assessment methods on images from benchmarked databases. The study provides insight into 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.