Statistical modeling in the shearlet domain for blind image quality assessment

The state-of-the-art blind image quality assessment (BIQA) metrics usually require a large amount of human scored images to train a regression model used to judge image quality, which makes the results are heavily dependent on the size of training data. In this paper, we present an efficient BIQA algorithm based on shearlet transform without using human scored images. This is mainly based on that the degradation of the image leads to significant variation in the spread discontinuities in all directions. However, shearlet transform has a strong ability to localize distributed discontinuities. The natural scene statistics (NSS) of shearlet coefficients are applicable to indicate the variation of image quality. Experimental results on benchmark databases illustrate that the proposed method has a good consistency with the subjective assessment of human beings.