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.