A perceptual image quality index based on global and double-random window similarity

As one of indispensable steps in vision applications, the evaluation of the perceptual quality of an image attracts significant attention in recent years. This paper presents an image quality assessment algorithm using global and local similarities as two complementary aspects. It is based on two assumptions: (1) The evaluation of information in double-random local windows can capture the quality of an image accurately. (2) This method is highly consistent with human visual system in response to visual stimuli. The final quality index is obtained by combining two components, where the local quality score is calculated by comparing various features in double-random windows, and the global quality score is obtained pixel by pixel in the image. Experimental results on four large-scale benchmark databases show the effective and stable performance of the proposed method.