首个用户生成HDR视频质量数据集，助力真实场景下画质评估
CHUG: Crowdsourced User-Generated HDR Video Quality Dataset
- 通过众包收集真实用户拍摄的HDR视频，模拟多分辨率与码率下的真实画质退化
- 包含5992段视频和21万+主观评分，覆盖856个原始视频源
- 为无参考HDR画质评估研究提供真实、多样、大规模的数据基准
高动态范围（HDR）视频以更高的亮度、对比度和色彩深度提升视觉体验。随着YouTube、TikTok等平台用户生成内容（UGC）激增，其在不同拍摄条件、编辑伪影和压缩失真下的HDR画质评估面临挑战。现有HDR-VQA数据集多聚焦专业制作内容（PGC），难以反映真实用户场景下的退化特征。为此，我们提出CHUG：首个针对用户生成HDR视频的大规模主观评估数据集。CHUG包含856个用户原始HDR视频，经多分辨率与码率转码后共生成5,992段视频，通过Amazon Mechanical Turk完成大规模主观评测，获得211,848条感知评分。该数据集为分析UGC特有的HDR退化现象提供了基准，有望推动无参考（NR）HDR-VQA研究的发展。数据集已公开：https://shreshthsaini.github.io/CHUG/
原文摘要 · Abstract (English)
High Dynamic Range (HDR) videos enhance visual experiences with superior brightness, contrast, and color depth. The surge of User-Generated Content (UGC) on platforms like YouTube and TikTok introduces unique challenges for HDR video quality assessment (VQA) due to diverse capture conditions, editing artifacts, and compression distortions. Existing HDR-VQA datasets primarily focus on professionally generated content (PGC), leaving a gap in understanding real-world UGC-HDR degradations. To address this, we introduce CHUG: Crowdsourced User-Generated HDR Video Quality Dataset, the first large-scale subjective study on UGC-HDR quality. CHUG comprises 856 UGC-HDR source videos, transcoded across multiple resolutions and bitrates to simulate real-world scenarios, totaling 5,992 videos. A large-scale study via Amazon Mechanical Turk collected 211,848 perceptual ratings. CHUG provides a benchmark for analyzing UGC-specific distortions in HDR videos. We anticipate CHUG will advance No-Reference (NR) HDR-VQA research by offering a large-scale, diverse, and real-world UGC dataset. The dataset is publicly available at: https://shreshthsaini.github.io/CHUG/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。