  Recent years have witnessed an ever-expandingvolume of user-generated content
(UGC) videos available on the Internet. Nevertheless, progress on perceptual
quality assessmentof UGC videos still remains quite limited. There are many
distinguished characteristics of UGC videos in the complete video production
and delivery chain, and one important property closely relevant to video
quality is that there does not exist the pristine source after they are
uploaded to the hosting platform,such that they often undergo multiple
compression stages before ultimately viewed. To facilitate the UGC video
quality assessment,we created a UGC video perceptual quality assessment
database. It contains 50 source videos collected from TikTok with diverse
content, along with multiple distortion versions generated bythe compression
with different quantization levels and coding standards. Subjective quality
assessment was conducted to evaluate the video quality. Furthermore, we
benchmark the database using existing quality assessment algorithms, and
potential roomis observed to future improve the accuracy of UGC video quality
measures.
