Image quality assessment based on matching pursuit

The objective assessment of image quality is an essential part of many visual processing systems. The challenge lies in evaluating the image quality consistently under subjective perceptions. In this paper, we propose a novel image quality metric based on the matching pursuit algorithm. Under the principle of structural information distortion, we assume that various structure data contributes differently to the single image quality score. Specifically, we decompose the reference image using matching pursuit with a separable 2D Gabor dictionary, thus obtaining structural information, and develop a characterization for this information and its importance. We then discuss the relationship between the structural distortion intensity and the subjective quality measurement on the energy scale. In experiments, we compare the performance of our algorithm with both subjective ratings and state-of-the-art objective methods on image datasets with multi-type distortions. The experimental results validate our proposed method.