A statistical evaluation of Sparsity-based Distance Measure (SDM) as an image quality assessment algorithm

Sparsity-based Distance Measure (SDM), a sparse reconstruction-based image similarity measure was recently proposed and shown to have promising applications in image classification, clustering and retrieval. In this paper, we present a statistical evaluation of SDM's performance as an image quality assessment (IQA) algorithm. This evaluation is carried out on the LIVE image database. We show that the SDM performs fairly in comparison with the state-of-the-art while possessing several attractive properties. Specifically, we demonstrate its robustness to rotation (90°, 180°), scaling, and combinations of distortions - properties that are highly desirable of any IQA algorithm.