Analyzing the Quality of Distorted Images by the Normalized Mutual Information Measure

This research explores how different types of distorting algorithms impact the Full-Reference image quality assessment, particularly when subjective quality evaluations are incorporated. We draw upon the TID2013 database, which contains 3000 images distorted by 24 distinct algorithms, in conjunction with Mean Opinion Scores (MOS) for quality ratings. We compare the results of Normalized Mutual Information (NMI) for image quality score with W2, based on Weibull distribution, the common PSNR similarity measure and MOS. We advocate for integrating of NMI into the repertoire of image quality assessment metrics.