@inproceedings{cee486f7f5d84340834d14c930a7e305,
title = "UGC-VIDEO: Perceptual Quality Assessment of User-Generated Videos",
abstract = "Recent years have witnessed an ever-expanding volume of user-generated content (UGC) videos available on the Internet. Nevertheless, progress on perceptual quality assessment of UGC videos still remains quite limited. A distinguished characteristic of UGC videos in the complete video production and delivery chain is that they often undergo multiple compression stages before ultimately viewed and there does not exist the pristine source after they are uploaded to the hosting platform. To facilitate the UGC video quality assessment (VQA), we create a UGC video perceptual quality assessment database. It contains 50 source videos collected from TikTok with diverse content, along with multiple transcoded versions generated by different coding standards and quantization levels. Subjective quality assessment has been conducted to evaluate the video quality. Furthermore, we benchmark the database using existing quality assessment algorithms, and potential room is observed to further improve the accuracy of UGC video quality measures.",
keywords = "database, user-generated content, video quality assessment",
author = "Yang Li and Shengbin Meng and Xinfeng Zhang and Shiqi Wang and Yue Wang and Siwei Ma",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020 ; Conference date: 06-08-2020 Through 08-08-2020",
year = "2020",
month = aug,
doi = "10.1109/MIPR49039.2020.00015",
language = "英语",
series = "Proceedings - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "35--38",
booktitle = "Proceedings - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020",
address = "美国",
}