1 research outputs found
Learn to Evaluate Image Perceptual Quality Blindly from Statistics of Self-similarity
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