Image Quality Assessment Using Image Description in Information Theory

In this paper, a novel image quality assessment (IQA) model using statistic features based on image information theory is proposed. Firstly, the image is decomposed into non-overlapped patches and transformed into the saliency information, specific information and entanglement information. Then considering the perception characteristics of human visual system, e.g. the center-surrounded of the receptive field and the contrast-gain masking process, have an important influence on image quality evaluation, the statistic features of the image information were employed to describe the image local contrast, structure, multi-scale and multi-direction properties, include the mean subtracted and contrast normalized (MSCN) features, the gradient magnitude (GM) features and the Laplacian of Gaussian (LOG) features. Finally, the mapping between the statistic features and the human subjective perception is established and used to measure the image quality. Experimental results on benchmark databases (LIVE, TID2013, CSIQ) indicate the rationality and validity of the proposed method.