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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.10331 |
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| _version_ | 1866913890040807424 |
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| author | Zhao, Fei Pan, Da Qi, Zelu Shi, Ping |
| author_facet | Zhao, Fei Pan, Da Qi, Zelu Shi, Ping |
| contents | In response to the rising prominence of the Metaverse, omnidirectional videos (ODVs) have garnered notable interest, gradually shifting from professional-generated content (PGC) to user-generated content (UGC). However, the study of audio-visual quality assessment (AVQA) within ODVs remains limited. To address this, we construct a dataset of UGC omnidirectional audio and video (A/V) content. The videos are captured by five individuals using two different types of omnidirectional cameras, shooting 300 videos covering 10 different scene types. A subjective AVQA experiment is conducted on the dataset to obtain the Mean Opinion Scores (MOSs) of the A/V sequences. After that, to facilitate the development of UGC-ODV AVQA fields, we construct an effective AVQA baseline model on the proposed dataset, of which the baseline model consists of video feature extraction module, audio feature extraction and audio-visual fusion module. The experimental results demonstrate that our model achieves optimal performance on the proposed dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Research on Audio-Visual Quality Assessment Dataset and Method for User-Generated Omnidirectional Video Zhao, Fei Pan, Da Qi, Zelu Shi, Ping Computer Vision and Pattern Recognition Image and Video Processing In response to the rising prominence of the Metaverse, omnidirectional videos (ODVs) have garnered notable interest, gradually shifting from professional-generated content (PGC) to user-generated content (UGC). However, the study of audio-visual quality assessment (AVQA) within ODVs remains limited. To address this, we construct a dataset of UGC omnidirectional audio and video (A/V) content. The videos are captured by five individuals using two different types of omnidirectional cameras, shooting 300 videos covering 10 different scene types. A subjective AVQA experiment is conducted on the dataset to obtain the Mean Opinion Scores (MOSs) of the A/V sequences. After that, to facilitate the development of UGC-ODV AVQA fields, we construct an effective AVQA baseline model on the proposed dataset, of which the baseline model consists of video feature extraction module, audio feature extraction and audio-visual fusion module. The experimental results demonstrate that our model achieves optimal performance on the proposed dataset. |
| title | Research on Audio-Visual Quality Assessment Dataset and Method for User-Generated Omnidirectional Video |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2506.10331 |