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Poster

Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference

Yan Zhong · Xingyu Wu · Li Zhang · Chenxi Yang · Tingting Jiang

Hall C 4-9 #2503
[ ]
Thu 25 Jul 2:30 a.m. PDT — 4 a.m. PDT

Abstract:

Due to the high cost of Image Quality Assessment (IQA) datasets, achieving robust generalization remains challenging for prevalent deep learning-based IQA methods. To address this, this paper proposes a novel end-to-end blind IQA method: Causal-IQA. Specifically, we first analyze the causal mechanisms in IQA tasks and construct a causal graph to understand the interplay and confounding effects between distortion types, image contents, and subjective human ratings. Then, through shifting the focus from correlations to causality, Causal-IQA aims to improve the estimation accuracy of image quality scores by mitigating the confounding effects using a causality-based optimization strategy. This optimization strategy is implemented on the sample subsets constructed by a Counterfactual Division process based on the Backdoor Criterion. Extensive experiments illustrate the superiority of Causal-IQA.

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