Universal no reference image quality assessment metrics based on local dependency

Abstract

No reference image quality assessment (NR-IQA) is to evaluate image quality blindly without the ground truth. Most of the emerging NR-IQA algorithms are only effective for some specific distortion. Universal metrics that can work for various categories of distortions have hardly been explored, and the algorithms available are not fully adequate in performance. In this paper, we study the local dependency (LD) characteristic of natural images, and propose two universal NR-IQA metrics: LD global scheme (LD-GS) and LD two-step scheme (LD-TS). We claim that the local dependency characteristic among wavelet coefficients is disturbed by various distortion processes, and the disturbances are strongly correlated to image qualities. Experimental results on LIVE database II demonstrate that both the proposed metrics are highly consistent with the human perception and outpace the state-of-the-art NR-IQA indexes and some full reference quality indicators for diverse distortions and across the entire database.