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arXiv 2002.122752020-02-27cs.MMeess.IV

Subjective Quality Assessment for YouTube UGC Dataset

Joong Gon Yim, Yilin Wang, Neil Birkbeck, Balu Adsumilli

英文摘要：

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.

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