Sparse Representation-Based Video Quality Assessment for Synthesized 3D Videos

Yun ZHANG, Huan ZHANG, Mei YU, Sam KWONG, Yo-Sung HO

Research output: Journal PublicationsJournal Article (refereed)peer-review

23 Citations (Scopus)

Abstract

The temporal flicker distortion is one of the most annoying noises in synthesized virtual view videos when they are rendered by compressed multi-view video plus depth in Three Dimensional (3D) video system. To assess the synthesized view video quality and further optimize the compression techniques in 3D video system, objective video quality assessment which can accurately measure the flicker distortion is highly needed. In this paper, we propose a full reference sparse representation-based video quality assessment method toward synthesized 3D videos. First, a synthesized video, treated as a 3D volume data with spatial (X-Y) and temporal (T) domains, is reformed and decomposed as a number of spatially neighboring temporal layers, i.e., X-T or Y-T planes. Gradient features in temporal layers of the synthesized video and strong edges of depth maps are used as key features in detecting the location of flicker distortions. Second, the dictionary learning and sparse representation for the temporal layers are then derived and applied to effectively represent the temporal flicker distortion. Third, a rank pooling method is used to pool all the temporal layer scores and obtain the score for the flicker distortion. Finally, the temporal flicker distortion measurement is combined with the conventional spatial distortion measurement to assess the quality of synthesized 3D videos. Experimental results on synthesized video quality database demonstrate our proposed method is significantly superior to the other state-of-the-art methods, especially on the view synthesis distortions induced from depth videos.
Original languageEnglish
Pages (from-to)509-524
JournalIEEE Transactions on Image Processing
Volume29
Early online date29 Jul 2019
DOIs
Publication statusPublished - 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61871372, Grant 61671258, and Grant 61672443, in part by the Guangdong NSF for Distinguished Young Scholar under Grant 2016A030306022, in part by the Key Project for Guangdong Provincial Science and Technology Development under Grant 2017B010110014, in part by the Shenzhen International Collaborative Research Project under Grant GJHZ20170314155404913, in part by the Shenzhen Science and Technology Program under Grant JCYJ20170811160212033 and Grant JCYJ20180507183823045, in part by the RGC General Research Fund (GRF) 9042322, 9042489 (CityU 11200116, 11206317), in part by the Guangdong International Science and Technology Cooperative Research Project under Grant 2018A050506063, and in part by the Membership of Youth Innovation Promotion Association, Chinese Academy of Sciences, under Grant 2018392.

Keywords

  • flicker distortion
  • sparse representation
  • synthesized view
  • temporal layer
  • Video quality assessment

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