No-reference image quality assessment based on sparse representation

The human visual system is sensitive to structural information in images, and modeling this information has been regarded as useful for predicting their perceptual quality. In this study, we propose a no-reference (NR) image quality assessment (IQA) method based on a sparse representation of the distribution of structural information. The grayscale fluctuation map of an image is first calculated and divided into patches of fixed size that are rearranged into column vectors, which are regarded as structural elements of the image. Following this, using sparse coding, these structural elements can be represented by sparse representation coefficients and a trained dictionary. By using the former, a probability vector for observing different elements in the trained dictionary can then be obtained. Finally, the prediction model is trained using support vector regression. The results of experiments to test the proposed method show that it can accurately predict humans' perception of image quality and is competitive in comparison with prevalent NR-IQA methods.