Due to the scale of social video sharing, User Generated Content (UGC) is getting more attention from academia and industry. To facilitate compression-related research on UGC, YouTube has released a large-scale dataset. The initial dataset only provided videos, limiting its use in quality assessment. We used a crowd-sourcing platform to collect subjective quality scores for this dataset. We analyzed the distribution of Mean Opinion Score (MOS) in various dimensions, and investigated some fundamental questions in video quality assessment, like the correlation between full video MOS and corresponding chunk MOS, and the influence of chunk variation in quality score aggregation.

中文翻译：

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YouTube UGC 数据集的主观质量评估

由于社交视频共享的规模，用户生成内容（UGC）越来越受到学术界和工业界的关注。为了促进 UGC 的压缩相关研究，YouTube 发布了一个大规模数据集。初始数据集仅提供视频，限制了其在质量评估中的使用。我们使用众包平台来收集该数据集的主观质量分数。我们分析了平均意见得分（MOS）在各个维度的分布，并研究了视频质量评估中的一些基本问题，例如全视频 MOS 与相应块 MOS 之间的相关性，以及块变化对质量得分聚合的影响。