Image quality assessment using a novel region smoothness measure

Abstract

One of the most efficient descriptions of image structure, which has been widely used in image quality assessment (IQA) studies, is the three-components model. Based on this model, the major structural components of an image are edges, textures and flat regions. We found that this model is basically derived from the abstract concept of image region smoothness. Indeed, each of these three components, is a particular region with special smoothness characteristics. Inspired by this fact, we developed an efficient general-purpose full-reference IQA technique, in which the amount of region smoothness degradation is gauged using our efficient MSER (maximally stable extremal region)-based region smoothness measure. For this, we build a block-based smoothness similarity map, and extract the image quality score, using a percentile averaging scheme. Experimental results are provided on popular benchmark databases, which confirm that the proposed approach has a reasonable prediction performance compared to the state-of-the-art image quality metrics.