# Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model

arXiv: https://arxiv.org/abs/1807.10990 · HTML: https://mlsanity.com/paper/1807.10990/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2018.

## Built on by (most cited first, 19 of 19)

- [Perceptual Video Quality Assessment: A Survey](https://mlsanity.com/paper/2402.03413/) (2024)
- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [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)
- [Panoramic Vision Transformer for Saliency Detection in 360deg Videos](https://mlsanity.com/paper/2209.08956/) (2022)
- [Spatial Attention-based Non-reference Perceptual Quality Prediction Network for Omnidirectional Images](https://mlsanity.com/paper/2103.06116/) (2021)
- [Perceptual Quality Assessment of Omnidirectional Audio-visual Signals](https://mlsanity.com/paper/2307.10813/) (2023)
- [Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessment](https://mlsanity.com/paper/2105.09143/) (2021)
- [Learning to Predict Salient Faces: A Novel Visual-Audio Saliency Model](https://mlsanity.com/paper/2103.15438/) (2021)
- [SHD360: A Benchmark Dataset for Salient Human Detection in 360deg Videos](https://mlsanity.com/paper/2105.11578/) (2021)
- [Blind VQA on 360deg Video via Progressively Learning from Pixels, Frames and Video](https://mlsanity.com/paper/2111.09503/) (2021)
- [Spherical Vision Transformers for Audio-Visual Saliency Prediction in 360-Degree Videos](https://mlsanity.com/paper/2508.20221/) (2025)
- [Perceptual Quality Assessment of Virtual Reality Videos in the Wild](https://mlsanity.com/paper/2206.08751/) (2022)
- [RL-ScanIQA: Reinforcement-Learned Scanpaths for Blind 360degImage Quality Assessment](https://mlsanity.com/paper/2603.14297/) (2026)
- [WinDB: HMD-free and Distortion-free Panoptic Video Fixation Learning](https://mlsanity.com/paper/2305.13901/) (2023)
- [A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution](https://mlsanity.com/paper/2506.06710/) (2025)
- [Omnidirectional Video Super-Resolution using Deep Learning](https://mlsanity.com/paper/2506.14803/) (2025)
- [Generating Realistic Synthetic Head Rotation Data for Extended Reality using Deep Learning](https://mlsanity.com/paper/2501.09050/) (2025)
- [Research on Audio-Visual Quality Assessment Dataset and Method for User-Generated Omnidirectional Video](https://mlsanity.com/paper/2506.10331/) (2025)
- [OpenSAL360: Open-Source Crowdsourcing Platform for Omnidirectional Video Saliency Collection](https://mlsanity.com/paper/2609.21480/) (2026)

## Abstract (arXiv)

Omnidirectional video enables spherical stimuli with the 360 × 180^ circ viewing range. Meanwhile, only the viewport region of omnidirectional video can be seen by the observer through head movement (HM), and an even smaller region within the viewport can be clearly perceived through eye movement (EM). Thus, the subjective quality of omnidirectional video may be correlated with HM and EM of human behavior. To fill in the gap between subjective quality and human behavior, this paper proposes a large-scale visual quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which collects 60 reference sequences and 540 impaired sequences. Our VQA-OV dataset provides not only the subjective quality scores of sequences but also the HM and EM data of subjects. By mining our dataset, we find that the subjective quality of omnidirectional video is indeed related to HM and EM. Hence, we develop a deep learning model, which embeds HM and EM, for objective VQA on omnidirectional video. Experimental results show that our model significantly improves the state-of-the-art performance of VQA on omnidirectional video.