User-generated Video Quality Assessment: A Subjective and Objective Study
release_42mgwpmc4bhs3aiszpmvqhb7tm
by
Yang Li, Shengbin Meng, Xinfeng Zhang, Shiqi Wang, Yue Wang, Siwei Ma
2020
Abstract
Recently, we have observed an exponential increase of user-generated content
(UGC) videos. The distinguished characteristic of UGC videos originates from
the video production and delivery chain, as they are usually acquired and
processed by non-professional users before uploading to the hosting platforms
for sharing. As such, these videos usually undergo multiple distortion stages
that may affect visual quality before ultimately being viewed. Inspired by the
increasing consensus that the optimization of the video coding and processing
shall be fully driven by the perceptual quality, in this paper, we propose to
study the quality of the UGC videos from both objective and subjective
perspectives. We first construct a UGC video quality assessment (VQA) database,
aiming to provide useful guidance for the UGC video coding and processing in
the hosting platform. The database contains source UGC videos uploaded to the
platform and their transcoded versions that are ultimately enjoyed by
end-users, along with their subjective scores. Furthermore, we develop an
objective quality assessment algorithm that automatically evaluates the quality
of the transcoded videos based on the corrupted reference, which is in
accordance with the application scenarios of UGC video sharing in the hosting
platforms. The information from the corrupted reference is well leveraged and
the quality is predicted based on the inferred quality maps with deep neural
networks (DNN). Experimental results show that the proposed method yields
superior performance. Both subjective and objective evaluations of the UGC
videos also shed lights on the design of perceptual UGC video coding.
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