  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.
