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An ICA-based approach for image quality assessment

In this paper, we propose an ICA-based approach for assessing image quality. Independent component analysis (ICA), which is a kind of fundamental statistical model for natural images, could model images as linear superpositions of basis images. The features given by ICA are suitable for image quality assessment because they resemble the representation given by simple-cells in the mammalian primary visual cortex. The steps of the proposed approach are listed concisely as follows: estimation of basis images in the ICA model; image features extraction from reference images and their corresponding distorted images; calculation of image quality scores or scales. Our experimental results show that the proposed method could achieve competitive performance with other two typical models, Structure SIMilarity (SSIM) and Visual Information Fidelity (VIF) by being tested on LIVE Subjective database. Some factors that may influence the performance results, such as the size of sliding window, the total number of image patches, are also discussed.