# Perceptual Quality Assessment of Omnidirectional Audio-visual Signals

arXiv: https://arxiv.org/abs/2307.10813 · HTML: https://mlsanity.com/paper/2307.10813/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Perceptual Quality Assessment of Omnidirectional Images](https://mlsanity.com/paper/2207.02674/) (2022)
- [Confusing Image Quality Assessment: Towards Better Augmented Reality Experience](https://mlsanity.com/paper/2204.04900/) (2022)
- [Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model](https://mlsanity.com/paper/1807.10990/) (2018)
- [Saliency in Augmented Reality](https://mlsanity.com/paper/2204.08308/) (2022)
- [Masked Autoencoders as Image Processors](https://mlsanity.com/paper/2303.17316/) (2023)
- [Perceptual Evaluation on Audio-visual Dataset of 360 Content](https://mlsanity.com/paper/2205.08007/) (2022)
- [Towards a Perceived Audiovisual Quality Model for Immersive Content](https://mlsanity.com/paper/2005.09309/) (2020)
- [Perceptual Evaluation of 360 Audiovisual Quality and Machine Learning Predictions](https://mlsanity.com/paper/2112.12273/) (2021)

## Built on by (most cited first, 12 of 12)

- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images](https://mlsanity.com/paper/2404.01024/) (2024)
- [Audio-visual Saliency for Omnidirectional Videos](https://mlsanity.com/paper/2311.05190/) (2023)
- [ESIQA: Perceptual Quality Assessment of Vision-Pro-based Egocentric Spatial Images](https://mlsanity.com/paper/2407.21363/) (2024)
- [How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and Model](https://mlsanity.com/paper/2408.05411/) (2024)
- [Spherical Vision Transformers for Audio-Visual Saliency Prediction in 360-Degree Videos](https://mlsanity.com/paper/2508.20221/) (2025)
- [MVBIND: Self-Supervised Music Recommendation For Videos Via Embedding Space Binding](https://mlsanity.com/paper/2405.09286/) (2024)
- [Quality-guided Skin Tone Enhancement for Portrait Photography](https://mlsanity.com/paper/2406.15848/) (2024)
- [ESVQA: Perceptual Quality Assessment of Egocentric Spatial Videos](https://mlsanity.com/paper/2412.20423/) (2024)
- [Quality Assessment and Distortion-aware Saliency Prediction for AI-Generated Omnidirectional Images](https://mlsanity.com/paper/2506.21925/) (2025)
- [Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation](https://mlsanity.com/paper/2412.06003/) (2024)
- [Research on Audio-Visual Quality Assessment Dataset and Method for User-Generated Omnidirectional Video](https://mlsanity.com/paper/2506.10331/) (2025)

## Abstract (arXiv)

Omnidirectional videos (ODVs) play an increasingly important role in the application fields of medical, education, advertising, tourism, etc. Assessing the quality of ODVs is significant for service-providers to improve the user's Quality of Experience (QoE). However, most existing quality assessment studies for ODVs only focus on the visual distortions of videos, while ignoring that the overall QoE also depends on the accompanying audio signals. In this paper, we first establish a large-scale audio-visual quality assessment dataset for omnidirectional videos, which includes 375 distorted omnidirectional audio-visual (A/V) sequences generated from 15 high-quality pristine omnidirectional A/V contents, and the corresponding perceptual audio-visual quality scores. Then, we design three baseline methods for full-reference omnidirectional audio-visual quality assessment (OAVQA), which combine existing state-of-the-art single-mode audio and video QA models via multimodal fusion strategies. We validate the effectiveness of the A/V multimodal fusion method for OAVQA on our dataset, which provides a new benchmark for omnidirectional QoE evaluation. Our dataset is available at https://github.com/iamazxl/OAVQA.