首个细粒度UGC视频质量评估模型，支持多维度质量分析
FineVQ: Fine-Grained User Generated Content Video Quality Assessment
- 构建首个包含6104段UGC视频的细粒度质量数据库FineVD
- 在FineVD等数据集上实现当前最优细粒度质量评分性能
- 适合视频平台优化推荐与内容审核场景使用
用户生成内容（UGC）视频的快速增长催生了高效视频质量评估（VQA）算法的迫切需求，以监控视频质量并指导优化与推荐流程。然而，现有VQA模型通常仅提供整体评分，缺乏服务于视频处理和推荐应用的细粒度标签。为应对挑战并推动UGC视频发展，我们建立了首个大规模细粒度视频质量评估数据库FineVD，包含6104段UGC视频，涵盖多个维度的细粒度质量评分与描述。基于该数据库，我们提出细粒度视频质量评估模型FineVQ，具备质量评级、评分与质量归因能力。大量实验表明，所提FineVQ可生成细粒度视频质量结果，在FineVD及其他常用UGC-VQA数据集上均达到领先性能。
原文摘要 · Abstract (English)
The rapid growth of user-generated content (UGC) videos has produced an urgent need for effective video quality assessment (VQA) algorithms to monitor video quality and guide optimization and recommendation procedures. However, current VQA models generally only give an overall rating for a UGC video, which lacks fine-grained labels for serving video processing and recommendation applications. To address the challenges and promote the development of UGC videos, we establish the first large-scale Fine-grained Video quality assessment Database, termed FineVD, which comprises 6104 UGC videos with fine-grained quality scores and descriptions across multiple dimensions. Based on this database, we propose a Fine-grained Video Quality assessment (FineVQ) model to learn the fine-grained quality of UGC videos, with the capabilities of quality rating, quality scoring, and quality attribution. Extensive experimental results demonstrate that our proposed FineVQ can produce fine-grained video-quality results and achieve state-of-the-art performance on FineVD and other commonly used UGC-VQA datasets.
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