Scaling-up Perceptual Video Quality Assessment

• URL: http://arxiv.org/abs/2505.22543v1
• Date: Wed, 28 May 2025 16:24:52 GMT
• Title: Scaling-up Perceptual Video Quality Assessment
• Authors: Ziheng Jia, Zicheng Zhang, Zeyu Zhang, Yingji Liang, Xiaorong Zhu, Chunyi Li, Jinliang Han, Haoning Wu, Bin Wang, Haoran Zhang, Guanyu Zhu, Qiyong Zhao, Xiaohong Liu, Guangtao Zhai, Xiongkuo Min,
• Abstract summary: We show how to efficiently build high-quality, human-in-the-loop VQA multi-modal instruction databases.<n>Our focus is on the technical and aesthetic quality dimensions, with abundant in-context instruction data to provide fine-grained VQA knowledge.<n>Our results demonstrate that our models achieve state-of-the-art performance in both quality understanding and rating tasks.
• Score: 54.691252495691955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The data scaling law has been shown to significantly enhance the performance of large multi-modal models (LMMs) across various downstream tasks. However, in the domain of perceptual video quality assessment (VQA), the potential of scaling law remains unprecedented due to the scarcity of labeled resources and the insufficient scale of datasets. To address this, we propose \textbf{OmniVQA}, an efficient framework designed to efficiently build high-quality, human-in-the-loop VQA multi-modal instruction databases (MIDBs). We then scale up to create \textbf{OmniVQA-Chat-400K}, the largest MIDB in the VQA field concurrently. Our focus is on the technical and aesthetic quality dimensions, with abundant in-context instruction data to provide fine-grained VQA knowledge. Additionally, we have built the \textbf{OmniVQA-MOS-20K} dataset to enhance the model's quantitative quality rating capabilities. We then introduce a \textbf{complementary} training strategy that effectively leverages the knowledge from datasets for quality understanding and quality rating tasks. Furthermore, we propose the \textbf{OmniVQA-FG (fine-grain)-Benchmark} to evaluate the fine-grained performance of the models. Our results demonstrate that our models achieve state-of-the-art performance in both quality understanding and rating tasks.

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