Sparse correlation coefficient for objective image quality assessment

Image quality assessment (IQA) is of fundamental importance to numerous image processing applications. Generally, image quality metrics (IQMs) regard image quality as fidelity or similarity with a reference image in some perceptual space. Such a full-reference IQA method is a kind of comparison that involves measuring the similarity or difference between two signals in a perceptually meaningful way. Modeling of the human visual system (HVS) has been regarded as the most suitable way to achieve perceptual quality predictions. In fact, natural image statistics can be an effective approach to simulate the HVS, since statistical models of natural images reveal some important response properties of the HVS. A useful statistical model of natural images is sparse coding, which is equivalent to independent component analysis (ICA). It provides a very good description of the receptive fields of simple cells in the primary visual cortex. Therefore, such a statistical model can be used to simulate the visual processing at the level of the visual cortex when designing IQMs. In this paper, we propose a fidelity criterion for IQA that relates image quality with the correlation between a reference and a distorted image in the form of sparse code. The proposed visual signal fidelity metric, which is called sparse correlation coefficient (SCC), is motivated by the need to capture the correlation between two sets of outputs from a sparse model of simple cell receptive fields. The SCC represents the correlation between two visual signals of images in a cortical visual space. The experimental results after both polynomial and logistic regression demonstrate that SCC is superior to recent state-of-the-art IQMs both in single-distortion and cross-distortion tests. (C) 2011 Elsevier B.V. All rights reserved.