Scaling Audio-Visual Quality Assessment Dataset via Crowdsourcing

• URL: http://arxiv.org/abs/2602.22659v1
• Date: Thu, 26 Feb 2026 06:18:11 GMT
• Title: Scaling Audio-Visual Quality Assessment Dataset via Crowdsourcing
• Authors: Renyu Yang, Jian Jin, Lili Meng, Meiqin Liu, Yilin Wang, Balu Adsumilli, Weisi Lin,
• Abstract summary: We propose a practical approach for AVQA dataset construction.<n>We design a crowdsourced subjective experiment framework for AVQA, breaks the constraints of in-lab settings, and achieves reliable annotation across varied environments.<n>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 sequences.
• Score: 62.250874651622574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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