  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 (HVS) 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 spatial locations of viewports, 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.
