Blind Image Quality Assessment Based on a New Feature of Nature Scene Statistics

Abstract

A recently proposed model, known as blind/referenceless image spatial quality evaluator (BRISQUE), achieves the state-of-the-art performance in context of blind image quality assessment (IQA). This model used the predefined generalized Gaussian distribution (GGD) to describe the regularity of natural scene statistics, introducing fitting errors due to variations of image contents. In this paper, a more generalized model is proposed to better characterize the regularity of extensive image contents, which is learned from the concatenated histograms of mean subtracted contrast normalized (MSCN) coefficients and pairwise products of MSCN coefficients of neighbouring pixels. The new feature based on MSCN shows its capability of preserving intrinsic distribution of image statistics. Consequently support vector machine regression (SVR) can map it to more accurate image quality scores. Experimental results show that the proposed approach achieves a slight gain from BRISQUE, which indicates the crafted GGD modelling step in BRISQUE is not essential for final performance.