# Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2002.09140 · HTML: https://mlsanity.com/paper/2002.09140/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 30 of 31)

- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (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)
- [Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment](https://mlsanity.com/paper/2305.10983/) (2023)
- [Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessment](https://mlsanity.com/paper/2105.09143/) (2021)
- [Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment](https://mlsanity.com/paper/2207.08124/) (2022)
- [ST360IQ: No-Reference Omnidirectional Image Quality Assessment with Spherical Vision Transformers](https://mlsanity.com/paper/2303.06907/) (2023)
- [No-Reference Point Cloud Quality Assessment via Graph Convolutional Network](https://mlsanity.com/paper/2411.07728/) (2024)
- [Perceptual Quality Assessment of 360^circ Images Based on Generative Scanpath Representation](https://mlsanity.com/paper/2309.03472/) (2023)
- [Omnidirectional Image Quality Captioning: A Large-scale Database and A New Model](https://mlsanity.com/paper/2502.15271/) (2025)
- [Perceptual Visual Quality Assessment: Principles, Methods, and Future Directions](https://mlsanity.com/paper/2503.00625/) (2025)
- [Blind VQA on 360deg Video via Progressively Learning from Pixels, Frames and Video](https://mlsanity.com/paper/2111.09503/) (2021)
- [Blind Omnidirectional Image Quality Assessment: Integrating Local Statistics and Global Semantics](https://mlsanity.com/paper/2302.12393/) (2023)
- [Learned Scanpaths Aid Blind Panoramic Video Quality Assessment](https://mlsanity.com/paper/2404.00252/) (2024)
- [Multitask Auxiliary Network for Perceptual Quality Assessment of Non-Uniformly Distorted Omnidirectional Images](https://mlsanity.com/paper/2501.11512/) (2025)
- [Perceptual Quality Assessment of Virtual Reality Videos in the Wild](https://mlsanity.com/paper/2206.08751/) (2022)
- [Perceptual Depth Quality Assessment of Stereoscopic Omnidirectional Images](https://mlsanity.com/paper/2408.10134/) (2024)
- [Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution](https://mlsanity.com/paper/2410.11506/) (2024)
- [Max360IQ: Blind Omnidirectional Image Quality Assessment with Multi-axis Attention](https://mlsanity.com/paper/2502.19046/) (2025)
- [Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Flexible and Effective Paradigm](https://mlsanity.com/paper/2503.06129/) (2025)
- [RL-ScanIQA: Reinforcement-Learned Scanpaths for Blind 360degImage Quality Assessment](https://mlsanity.com/paper/2603.14297/) (2026)
- [Subjective and Objective Quality-of-Experience Evaluation Study for Live Video Streaming](https://mlsanity.com/paper/2409.17596/) (2024)
- [Computational Analysis of Degradation Modeling in Blind Panoramic Image Quality Assessment](https://mlsanity.com/paper/2503.03255/) (2025)
- [Surveillance Facial Image Quality Assessment: A Multi-dimensional Dataset and Lightweight Model](https://mlsanity.com/paper/2602.07403/) (2026)
- [Bayesian Graph Convolutional Network for Traffic Prediction](https://mlsanity.com/paper/2104.00488/) (2021)
- [PKU-AIGIQA-4K: A Perceptual Quality Assessment Database for Both Text-to-Image and Image-to-Image AI-Generated Images](https://mlsanity.com/paper/2404.18409/) (2024)
- [Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement](https://mlsanity.com/paper/2412.12667/) (2024)
- [Hierarchical Graph Attention Network for No-Reference Omnidirectional Image Quality Assessment](https://mlsanity.com/paper/2508.09843/) (2025)
- [Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Unified and Generalized Approach](https://mlsanity.com/paper/2604.23953/) (2026)
- [Ultra-High-Definition Image Quality Assessment via Graph Representation Learning](https://mlsanity.com/paper/2605.22192/) (2026)

## Abstract (arXiv)

Quality assessment of omnidirectional images has become increasingly urgent due to the rapid growth of virtual reality applications. Different from traditional 2D images and videos, omnidirectional contents can provide consumers with freely changeable viewports and a larger field of view covering the 360circ×180circ spherical surface, which makes the objective quality assessment of omnidirectional images more challenging. In this paper, motivated by the characteristics of the human vision system (HVS) and the viewing process of omnidirectional contents, we propose a novel Viewport oriented Graph Convolution Network (VGCN) for blind omnidirectional image quality assessment (IQA). Generally, observers tend to give the subjective rating of a 360-degree image after passing and aggregating different viewports information when browsing the spherical scenery. Therefore, in order to model the mutual dependency of viewports in the omnidirectional image, we build a spatial viewport graph. Specifically, the graph nodes are first defined with selected viewports with higher probabilities to be seen, which is inspired by the HVS that human beings are more sensitive to structural information. Then, these nodes are connected by spatial relations to capture interactions among them. Finally, reasoning on the proposed graph is performed via graph convolutional networks. Moreover, we simultaneously obtain global quality using the entire omnidirectional image without viewport sampling to boost the performance according to the viewing experience. Experimental results demonstrate that our proposed model outperforms state-of-the-art full-reference and no-reference IQA metrics on two public omnidirectional IQA databases.