User-generated Video Quality Assessment : A Subjective and Objective Study

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Yang Li
  • Shengbin Meng
  • Xinfeng Zhang
  • Yue Wang
  • Siwei Ma

Related Research Unit(s)


Original languageEnglish
Number of pages13
Journal / PublicationIEEE Transactions on Multimedia
Online published29 Oct 2021
Publication statusOnline published - 29 Oct 2021


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.

Research Area(s)

  • Convolutional neural networks, Databases, deep neural network, Distortion, Feature extraction, Quality assessment, Three-dimensional displays, User-generated content, video quality assessment, Video recording