Learn to Evaluate Image Perceptual Quality Blindly from Statistics of Self-similarity
This addresses the problem of evaluating image quality blindly for applications like image processing, though it appears incremental as it builds on existing natural scene statistics approaches.
The paper tackles blind image quality assessment (BIQA) by proposing a method based on statistics of self-similarity (SOS) to predict perceptual quality without reference images, achieving competitive performance in experiments.
Among the various image quality assessment (IQA) tasks, blind IQA (BIQA) is particularly challenging due to the absence of knowledge about the reference image and distortion type. Features based on natural scene statistics (NSS) have been successfully used in BIQA, while the quality relevance of the feature plays an essential role to the quality prediction performance. Motivated by the fact that the early processing stage in human visual system aims to remove the signal redundancies for efficient visual coding, we propose a simple but very effective BIQA method by computing the statistics of self-similarity (SOS) in an image. Specifically, we calculate the inter-scale similarity and intra-scale similarity of the distorted image, extract the SOS features from these similarities, and learn a regression model to map the SOS features to the subjective quality score. Extensive experiments demonstrate very competitive quality prediction performance and generalization ability of the proposed SOS based BIQA method.