Assessing Visual Quality of Omnidirectional Videos
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Abstract (arXiv)
In contrast with traditional video, omnidirectional video enables spherical viewing direction with support for head-mounted displays, providing an interactive and immersive experience. Unfortunately, to the best of our knowledge, there are few visual quality assessment (VQA) methods, either subjective or objective, for omnidirectional video coding. This paper proposes both subjective and objective methods for assessing quality loss in encoding omnidirectional video. Specifically, we first present a new database, which includes the viewing direction data from several subjects watching omnidirectional video sequences. Then, from our database, we find a high consistency in viewing directions across different subjects. The viewing directions are normally distributed in the center of the front regions, but they sometimes fall into other regions, related to video content. Given this finding, we present a subjective VQA method for measuring difference mean opinion score (DMOS) of the whole and regional omnidirectional video, in terms of overall DMOS (O-DMOS) and vectorized DMOS (V-DMOS), respectively. Moreover, we propose two objective VQA methods for encoded omnidirectional video, in light of human perception characteristics of omnidirectional video. One method weighs the distortion of pixels with regard to their distances to the center of front regions, which considers human preference in a panorama. The other method predicts viewing directions according to video content, and then the predicted viewing directions are leveraged to allocate weights to the distortion of each pixel in our objective VQA method. Finally, our experimental results verify that both the subjective and objective methods proposed in this paper advance state-of-the-art VQA for omnidirectional video.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2017.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 3; 2024: 2; 2025: 1; 2026: 1.
11 papers.
- 2023AGIQA-3K: An Open Database for AI-Generated Image Quality Assessmentcited by 142
- 2022Deep Learning for Omnidirectional Vision: A Survey and New Perspectivescited by 38
- 2025A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Visioncited by 23
- 2021No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalnesscited by 20
- 2023Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessmentcited by 17
- 2021Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessmentcited by 10
- 2021Blind VQA on 360deg Video via Progressively Learning from Pixels, Frames and Videocited by 4
- 2023Spherical Vision Transformer for 360-degree Video Saliency Predictioncited by 2
- 2024Perceptual Depth Quality Assessment of Stereoscopic Omnidirectional Imagescited by 2
- 2024A review on Machine Learning based User-Centric Multimedia Streaming Techniquescited by 1
- 2026Prompt-Adapter Context Routing for Parameter-Efficient Multi-Shot Long Video Extrapolationcited by 0
- 2026Prompt-Adapter Context Routing for Parameter-Efficient Multi-Shot Long Video Extrapolation7 Jul 2026
- 2025A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision11 Feb 2025
- 2024A review on Machine Learning based User-Centric Multimedia Streaming Techniques24 Nov 2024
- 2024Perceptual Depth Quality Assessment of Stereoscopic Omnidirectional Images19 Aug 2024
- 2023Spherical Vision Transformer for 360-degree Video Saliency Prediction24 Aug 2023
- 2023AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment7 Jun 2023
- 2023Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment18 May 2023
- 2022Deep Learning for Omnidirectional Vision: A Survey and New Perspectives21 May 2022
- 2021Blind VQA on 360deg Video via Progressively Learning from Pixels, Frames and Video18 Nov 2021
- 2021Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessment19 May 2021
- 2021No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness22 Feb 2021