Subjective Quality Assessment for YouTube UGC Dataset
This work addresses the need for quality assessment data in UGC video compression research, though it is incremental as it extends an existing dataset with subjective scores.
The researchers tackled the lack of subjective quality scores in YouTube's UGC dataset by collecting Mean Opinion Scores via crowd-sourcing, analyzing MOS distributions and investigating correlations between full video and chunk MOS, with results including specific analyses of chunk variation effects.
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.