Adaptive hypergraph convolutional network for no-reference 360-degree image quality assessment

Fu, Jun, Hou, Chen, Zhou, Wei, Xu, Jiahua and Chen, Zhibo 2022. Adaptive hypergraph convolutional network for no-reference 360-degree image quality assessment. Presented at: MM '22: The 30th ACM International Conference on Multimedia, Lisboa Portugal, 10-14 October 2022. MM '22: Proceedings of the 30th ACM International Conference on Multimedia. Association for Computing Machinery, pp. 961-969. 10.1145/3503161.3548337

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Abstract

In no-reference 360-degree image quality assessment (NR 360IQA), graph convolutional networks (GCNs), which model interactions between viewports through graphs, have achieved impressive performance. However, prevailing GCN-based NR 360IQA methods suffer from three main limitations. First, they only use high-level features of the distorted image to regress the quality score, while the human visual system scores the image based on hierarchical features. Second, they simplify complex high-order interactions between viewports in a pairwise fashion through graphs. Third, in the graph construction, they only consider the spatial location of the viewport, ignoring its content characteristics. Accordingly, to address these issues, we propose an adaptive hypergraph convolutional network for NR 360IQA, denoted as AHGCN. Specifically, we first design a multi-level viewport descriptor for extracting hierarchical representations from viewports. Then, we model interactions between viewports through hypergraphs, where each hyperedge connects two or more viewports. In the hypergraph construction, we build a location-based hyperedge and a content-based hyperedge for each viewport. Experimental results on two public 360IQA databases demonstrate that our proposed approach has a clear advantage over state-of-the-art full-reference and no-reference IQA models.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computing Machinery
ISBN:	978-1-4503-9203-7
Last Modified:	26 Sep 2023 13:30
URI:	https://orca.cardiff.ac.uk/id/eprint/162054

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