论文标题
YouTube UGC数据集的主观质量评估
Subjective Quality Assessment for YouTube UGC Dataset
论文作者
论文摘要
由于社交视频共享的规模，用户生成的内容（UGC）从学术界和行业中获得了更多关注。为了促进与UGC的压缩相关研究，YouTube发布了一个大规模数据集。最初的数据集仅提供视频，从而限制了其在质量评估中的使用。我们使用了一个众包平台来收集此数据集的主观质量分数。我们在各个维度上分析了平均意见评分（MOS）的分布，并研究了视频质量评估中的一些基本问题，例如完整的视频MOS与相应的块MOS之间的相关性以及块变化在质量分数聚合中的影响。
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.
