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.

翻译：良好的失真表示对于深度盲图像质量评估（BIQA）的成功至关重要。然而，以往大多数方法未能有效建模失真之间的关系，或同一失真类型但不同失真程度样本的分布。本文从感知图像质量与失真相关因素（如失真类型与程度）的关系分析入手，提出一种用于图像质量评估的失真图表示学习框架——GraphIQA，其中每个失真被表示为一张图，即失真图表示。通过学习不同失真图表示之间的对比关系，可以区分失真类型，并推断同一失真图表示中不同级别样本的排序分布。具体而言，我们开发了两个子网络来学习失真图表示：a) 类型判别网络，旨在将失真图表示嵌入紧凑编码，以更好区分失真类型并学习类型间关系；b) 模糊预测网络，旨在提取失真图表示中样本的分布特征，并基于高斯先验预测模糊程度。实验表明，我们的GraphIQA在多个涵盖合成与真实失真的基准数据集上达到了最先进的性能。