GOP-level transmission distortion modeling for mobile streaming video

  • Authors:

  • Chongyang Zhang;Hua Yang;Songyu Yu;Xiaokang Yang

  • Affiliations:

  • Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, PR China and Shanghai Key Laboratory of Digital Media Processing and Transmissions, Sha ...;Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, PR China and Shanghai Key Laboratory of Digital Media Processing and Transmissions, Sha ...;Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, PR China and Shanghai Key Laboratory of Digital Media Processing and Transmissions, Sha ...;Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, PR China and Shanghai Key Laboratory of Digital Media Processing and Transmissions, Sha ...

  • Venue:
  • Image Communication
  • Year:
  • 2008

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Abstract

Unequal loss protection is an effective tool in delivering compressed video streaming over packet-switched networks robustly. A critical component in any unequal-loss-protection scheme is a metric for evaluating the importance of different frames in a Group-Of-Pictures (GOP). In the case of video streaming over 3G mobile networks, packet loss usually corresponds to whole-frame loss due to low bandwidth and small picture size, which results in high error rates and thus most of the existing low-complexity transmission-distortion-estimate models may be ineffective. In this paper, we firstly develop a recursive algorithm to compute the GOP-level transmission distortion at pixel-level precision using pre-computed video information. Based on the study on the propagating behavior of the whole-frame-loss transmission distortion, we then propose a piecewise linear-fitting approach to achieve low-complexity transmission distortion modeling. The simulation results demonstrate that the proposed two models are accurate and robust. The proposed transmission distortion models are fast and accurate importance assessment tools in allocating limited channel resources optimally for the mobile streaming video.