Blind Image Quality Assessment Based on Natural Redundancy Statistics

Blind image quality assessment (BIQA) aims to evaluate the perceptual quality of a distorted image without information regarding its reference image and the distortion type. Existing BIQA methods usually predict the image quality by employing natural scene statistic (NSS), which is derived from the statistical distributions of image coefficients by reducing the redundancies in a transformed domain. Contrary to these methods, we directly measure the redundancy existing in a natural image and compute the natural redundancy statistics (NRS) to capture the distortion degree. Specially, we utilize the singular value decomposition (SVD) and asymmetric generalized Gaussian distribution (AGGD) modeling to obtain NRS from opponent color spaces, and learn a regression model to map the NRS features to the subjective quality score. Extensive experiments demonstrate very competitive quality prediction performance and generalization ability of the proposed method.