  Learning-based blind image quality assessment (BIQA) methods have recently
drawn much attention for their superior performance compared to traditional
methods. However, most of them do not effectively leverage the relationship
between distortion-related factors, showing limited feature representation
capacity. In this paper, we show that human perceptual quality is highly
correlated with distortion type and degree, and is biased by image content in
most IQA systems. Based on this observation, we propose a Distortion Graph
Representation (DGR) learning framework for IQA, called GraphIQA. In GraphIQA,
each distortion is represented as a graph i.e. DGR. One can distinguish
distortion types by comparing different DGRs, and predict image quality by
learning the relationship between different distortion degrees in DGR.
Specifically, we develop two sub-networks to learn the DGRs: a) Type
Discrimination Network (TDN) that embeds DGR into a compact code for better
discriminating distortion types; b) Fuzzy Prediction Network (FPN) that
extracts the distributional characteristics of the samples in DGR and predicts
fuzzy degrees based on a Gaussian prior. Experiments show that our GraphIQA
achieves the state-of-the-art performance on many benchmark datasets of both
synthetic and authentic distortion.
