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

arXiv: https://arxiv.org/abs/2508.09843 · HTML: https://mlsanity.com/paper/2508.09843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective](https://mlsanity.com/paper/2303.14968/) (2023)
- [Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild](https://mlsanity.com/paper/2005.13983/) (2020)
- [Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration](https://mlsanity.com/paper/2408.15994/) (2024)
- [Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method](https://mlsanity.com/paper/2303.15166/) (2023)
- [Perceptual Quality Assessment of Omnidirectional Images](https://mlsanity.com/paper/2207.02674/) (2022)
- [Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networks](https://mlsanity.com/paper/2002.09140/) (2020)
- [UniUIR: Considering Underwater Image Restoration as An All-in-One Learner](https://mlsanity.com/paper/2501.12981/) (2025)
- [No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness](https://mlsanity.com/paper/2102.11393/) (2021)
- [Spatial Attention-based Non-reference Perceptual Quality Prediction Network for Omnidirectional Images](https://mlsanity.com/paper/2103.06116/) (2021)
- [Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports](https://mlsanity.com/paper/1908.10079/) (2019)
- [Subjective and Objective Quality Assessment of Non-Uniformly Distorted Omnidirectional Images](https://mlsanity.com/paper/2501.11511/) (2025)
- [Omnidirectional Image Quality Captioning: A Large-scale Database and A New Model](https://mlsanity.com/paper/2502.15271/) (2025)
- [Multitask Auxiliary Network for Perceptual Quality Assessment of Non-Uniformly Distorted Omnidirectional Images](https://mlsanity.com/paper/2501.11512/) (2025)

## Abstract (arXiv)

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.