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Among the various image quality assessment (IQA) tasks, blind IQA (BIQA) isparticularly challenging due to the absence of knowledge about the referenceimage and distortion type. Features based on natural scene statistics (NSS)have been successfully used in BIQA, while the quality relevance of the featureplays an essential role to the quality prediction performance. Motivated by thefact that the early processing stage in human visual system aims to remove thesignal redundancies for efficient visual coding, we propose a simple but veryeffective BIQA method by computing the statistics of self-similarity (SOS) inan image. Specifically, we calculate the inter-scale similarity and intra-scalesimilarity of the distorted image, extract the SOS features from thesesimilarities, and learn a regression model to map the SOS features to thesubjective quality score. Extensive experiments demonstrate very competitivequality prediction performance and generalization ability of the proposed SOSbased BIQA method.

Learn to Evaluate Image Perceptual Quality Blindly from Statistics of Self-similarity