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

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 assess...

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Bibliographic Details
Published in:Maǧallaẗ al-abḥath al-handasiyyaẗ 2023, Vol.11 (1 A), p.133-155
Main Authors: Varghese, Justin, Saini, Rajesh Kumar, Jain, Neeraj Kumar, Mittal, Preeti
Format: Article
Language:English
Online Access:Get full text
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Summary: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.
ISSN:2307-1877
2307-1885
DOI:10.36909/jer.11885