  A good distortion representation is crucial for the success of deep blind
image quality assessment (BIQA). However, most previous methods do not
effectively model the relationship between distortions or the distribution of
samples with the same distortion type but different distortion levels. In this
work, we start from the analysis of the relationship between perceptual image
quality and distortion-related factors, such as distortion types and levels.
Then, we propose a Distortion Graph Representation (DGR) learning framework for
IQA, named GraphIQA, in which each distortion is represented as a graph, i.e.,
DGR. One can distinguish distortion types by learning the contrast relationship
between these different DGRs, and infer the ranking distribution of samples
from different levels in a DGR. Specifically, we develop two sub-networks to
learn the DGRs: a) Type Discrimination Network (TDN) that aims to embed DGR
into a compact code for better discriminating distortion types and learning the
relationship between types; b) Fuzzy Prediction Network (FPN) that aims to
extract the distributional characteristics of the samples in a 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 distortions.
