With the fast proliferation of online video sites and social media platforms, user, professionally and occupationally generated content (UGC, PGC, OGC) videos are streamed and explosively shared over the Internet. Consequently, it is urgent to monitor the content quality of these Internet videos to guarantee the user experience. However, most existing modern video quality assessment (VQA) databases only include UGC videos and cannot meet the demands for other kinds of Internet videos with real-world distortions.
To this end, we collect 1,072 videos from Youku, a leading Chinese video hosting service platform, to establish the Internet video quality assessment database (Youku-V1K). A special sampling method based on several quality indicators is adopted to maximize the content and distortion diversities within a limited database, and a probabilistic graphical model is applied to recover reliable labels from noisy crowdsourcing annotations.
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References and Citation
If you are using this database for any publications, please DO consider to cite our work.
JX2021: Jiahua Xu, Jing Li, Xingguang Zhou, Wei Zhou, Baichao Wang, Zhibo Chen, “Perceptual Quality Assessment of Internet Videos”, MM ‘21: Proceedings of the 29th ACM International Conference on Multimedia, October 2021, Pages 1248–1257.