Scaling Audio-Visual Quality Assessment Dataset via Crowdsourcing
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918357263974400 |
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| author | Yang, Renyu Jin, Jian Meng, Lili Liu, Meiqin Wang, Yilin Adsumilli, Balu Lin, Weisi |
| author_facet | Yang, Renyu Jin, Jian Meng, Lili Liu, Meiqin Wang, Yilin Adsumilli, Balu Lin, Weisi |
| contents | Audio-visual quality assessment (AVQA) research has been stalled by limitations of existing datasets: they are typically small in scale, with insufficient diversity in content and quality, and annotated only with overall scores. These shortcomings provide limited support for model development and multimodal perception research. We propose a practical approach for AVQA dataset construction. First, we design a crowdsourced subjective experiment framework for AVQA, breaks the constraints of in-lab settings and achieves reliable annotation across varied environments. Second, a systematic data preparation strategy is further employed to ensure broad coverage of both quality levels and semantic scenarios. Third, we extend the dataset with additional annotations, enabling research on multimodal perception mechanisms and their relation to content. Finally, we validate this approach through YT-NTU-AVQ, the largest and most diverse AVQA dataset to date, consisting of 1,620 user-generated audio and video (A/V) sequences. The dataset and platform code are available at https://github.com/renyu12/YT-NTU-AVQ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22659 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scaling Audio-Visual Quality Assessment Dataset via Crowdsourcing Yang, Renyu Jin, Jian Meng, Lili Liu, Meiqin Wang, Yilin Adsumilli, Balu Lin, Weisi Computer Vision and Pattern Recognition Multimedia Audio-visual quality assessment (AVQA) research has been stalled by limitations of existing datasets: they are typically small in scale, with insufficient diversity in content and quality, and annotated only with overall scores. These shortcomings provide limited support for model development and multimodal perception research. We propose a practical approach for AVQA dataset construction. First, we design a crowdsourced subjective experiment framework for AVQA, breaks the constraints of in-lab settings and achieves reliable annotation across varied environments. Second, a systematic data preparation strategy is further employed to ensure broad coverage of both quality levels and semantic scenarios. Third, we extend the dataset with additional annotations, enabling research on multimodal perception mechanisms and their relation to content. Finally, we validate this approach through YT-NTU-AVQ, the largest and most diverse AVQA dataset to date, consisting of 1,620 user-generated audio and video (A/V) sequences. The dataset and platform code are available at https://github.com/renyu12/YT-NTU-AVQ |
| title | Scaling Audio-Visual Quality Assessment Dataset via Crowdsourcing |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2602.22659 |