SUBJECTIVE QUALITY ASSESSMENT FOR YOUTUBE UGC DATASET

被引：0
作者：
Yim, Joong Gon [1 ]
Wang, Yilin [1 ]
Birkbeck, Neil [1 ]
Adsumilli, Balu [1 ]
机构：
[1] Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 USA
来源：
2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2020年
关键词：
Video quality assessment; User Generated Content; Crowd-sourcing;
D O I：
10.1109/icip40778.2020.9191194
中图分类号：
TB8 [摄影技术];
学科分类号：
0804 ;
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 [1]. 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.
引用
收藏
页码：131 / 135
页数：5
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