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arXiv · 2508.09843

Hierarchical Graph Attention Network for No-Reference Omnidirectional Image Quality Assessment

Hao Yang·Xu Zhang·Jiaqi Ma·Linwei Zhu·Yun Zhang·Huan Zhang
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Abstract

Current Omnidirectional Image Quality Assessment (OIQA) methods struggle to evaluate locally non-uniform distortions due to inadequate modeling of spatial variations in quality and ineffective feature representation capturing both local details and global context. To address this, we propose a graph neural network-based OIQA framework that explicitly models structural relationships between viewports to enhance perception of spatial distortion non-uniformity. Our approach employs Fibonacci sphere sampling to generate viewports with well-structured topology, representing each as a graph node. Multi-stage feature extraction networks then derive high-dimensional node representation. To holistically capture spatial dependencies, we integrate a Graph Attention Network (GAT) modeling fine-grained local distortion variations among adjacent viewports, and a graph transformer capturing long-range quality interactions across distant regions. Extensive experiments on two large-scale OIQA databases with complex spatial distortions demonstrate that our method significantly outperforms existing approaches, confirming its effectiveness and strong generalization capability.

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Hao Yang, Xu Zhang, Jiaqi Ma, Linwei Zhu, Yun Zhang, Huan Zhang. 2025-08-13. Hierarchical Graph Attention Network for No-Reference Omnidirectional Image Quality Assessment. https://arxiv.org/abs/2508.09843

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