MVQA-68K: A Multi-dimensional and Causally-annotated Dataset with Quality Interpretability for Video Assessment

• URL: http://arxiv.org/abs/2509.11589v1
• Date: Mon, 15 Sep 2025 05:16:54 GMT
• Title: MVQA-68K: A Multi-dimensional and Causally-annotated Dataset with Quality Interpretability for Video Assessment
• Authors: Yanyun Pu, Kehan Li, Zeyi Huang, Zhijie Zhong, Kaixiang Yang,
• Abstract summary: Video quality assessment (VQA) is becoming increasingly crucial for selecting high-quality videos from large-scale datasets used in pre-training.<n>We introduce MVQA-68K, a novel multi-dimensional VQA dataset comprising over 68,000 carefully annotated videos.<n>Experiments demonstrate that MVQA-68K significantly enhances the performance of various large language models (MLLMs) on the VQA task.
• Score: 14.705190484805962
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid advancement of video generation models such as Sora, video quality assessment (VQA) is becoming increasingly crucial for selecting high-quality videos from large-scale datasets used in pre-training. Traditional VQA methods, typically producing single numerical scores, often lack comprehensiveness and interpretability. To address these challenges, we introduce MVQA-68K, a novel multi-dimensional VQA dataset comprising over 68,000 carefully annotated videos, covering seven essential quality dimensions: overall aesthetics, camera movement, dynamic degree, texture detail, composition, visual quality, and factual consistency. Each annotation includes detailed chain-of-thought reasoning to facilitate interpretability and comprehensive understanding. Extensive experiments demonstrate that MVQA-68K significantly enhances the performance of various multimodal large language models (MLLMs) on the VQA task, achieving state-of-the-art results not only on our internal test set (Fig.1) but also on public benchmarks including LSVQ-test, LSVQ-1080p, and LIVE-VQC. Meantime, incorporating explicit reasoning process during VQA training substantially boosts the zero-shot generalization. Code and dataset will be available at github: https://github.com/Controller01-ai/MVQA-68K

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