A novel blind image quality assessment metric and its feature selection strategy

We recently proposed a natural scene statistics based image quality assessment (IQA) metric named STAIND, which extracts nearly independent components from natural image, i.e., the divisive normalization transform (DNT) coefficients, and evaluates perceptual quality of distortion image by measuring the degree of dependency between neighboring DNT coefficients. To improve the performance of STAIND, its feature selection strategy is thoroughly analyzed in this paper. The basic neighbor relationships in STAIND include scale, orientation and space. By analyzing the joint histograms of different neighborships and comparing the IQA model performances of diverse feature combination schemes on the publicly available databases such as LIVE, CSIQ and TID2008, we draw the following conclusions: 1) Spatial neighbor relationship contributes most to the model design, scale neighborship takes second place, and orientation neighbors might introduce negative effects; 2) In spatial domain, second order spatial neighbors are beneficial supplements to first order spatial neighbors; 3) The combined neighborship between the scales, spaces and the introduced spatial parents is very efficient for blind IQA metrics design.