GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment

Sun, Simeng, Yu, Tao, Xu, Jiahua, Zhou, Wei and Chen, Zhibo 2023. GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment. IEEE Transactions on Multimedia 25 , pp. 2912-2925. 10.1109/TMM.2022.3152942

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Abstract

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 can 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 state-of-the-art performance on many benchmark datasets of both synthetic and authentic distortions.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1520-9210
Date of Acceptance:	13 February 2022
Last Modified:	16 Aug 2023 10:00
URI:	https://orca.cardiff.ac.uk/id/eprint/161689

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