中国邮电高校学报(英文) ›› 2009, Vol. 16 ›› Issue (5): 92-102.doi: 10.1016/S1005-8885(08)60274-5

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Key-frame reference selection for non-feedback video communication

彭强,张蕾,YANG Tian-wu,Jim X. Chen,Zhu Ce   

  1. School of Information Science & Technology, Southwest Jiaotong University, Chengdu 610031, China
  • 收稿日期:2008-12-30 修回日期:1900-01-01 出版日期:2009-10-30
  • 通讯作者: 张蕾

Key-frame reference selection for non-feedback video communication

PENG Qiang, ZHANG Lei, YANG Tian-wu, CHEN Jim X, ZHU Ce   

  1. School of Information Science & Technology, Southwest Jiaotong University, Chengdu 610031, China
  • Received:2008-12-30 Revised:1900-01-01 Online:2009-10-30
  • Contact: ZHANG Lei

摘要:

This article addresses the problem of reference picture optimization in video communication over error prone networks. A novel estimation model for transmission distortion is proposed. This model is capable of recursively estimating the overall end-to-end distortion caused by quantization, error propagation, and error concealment. Simulation results show that this model can accurately estimate channel distortion. Then, based on the distortion estimation model, a new non-feedback key-frame reference picture selection (KRPS) algorithm is developed. The optimum reference picture minimizes the transmission distortion under the rate-distortion optimization framework. Extensive experiment results demonstrate that the proposed KRPS algorithm substantially achieves more peak signal to noise ratio (PSNR) gain over traditional prediction, especially in low bit-rate transmission.

关键词:

End-to-end;distortion,;key-frame,;reference;picture;selection,;error;resilience

Abstract:

This article addresses the problem of reference picture optimization in video communication over error prone networks. A novel estimation model for transmission distortion is proposed. This model is capable of recursively estimating the overall end-to-end distortion caused by quantization, error propagation, and error concealment. Simulation results show that this model can accurately estimate channel distortion. Then, based on the distortion estimation model, a new non-feedback key-frame reference picture selection (KRPS) algorithm is developed. The optimum reference picture minimizes the transmission distortion under the rate-distortion optimization framework. Extensive experiment results demonstrate that the proposed KRPS algorithm substantially achieves more peak signal to noise ratio (PSNR) gain over traditional prediction, especially in low bit-rate transmission.

Key words:

End-to-end distortion;key-frame;reference picture selection;error resilience