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Multimedia Systems

, Volume 4, Issue 6, pp 357–369 | Cite as

Traffic and video quality with adaptive neural compression

  • Erol Gelenbe
  • Mert Sungur
  • Christopher Cramer
  • Pamir Gelenbe

Abstract.

Video sequences are major sources of traffic for broadband ISDN networks, and video compression is fundamental to the efficient use of such networks. We present a novel neural method to achieve real-time adaptive compression of video. This tends to maintain a target quality of the decompressed image specified by the user. The method uses a set of compression/decompression neural networks of different levels of compression, as well as a simple motion-detection procedure. We describe the method and present experimental data concerning its performance and traffic characteristics with real video sequences. The impact of this compression method on ATM-cell traffic is also investigated and measurement data are provided.

Key words:Compression/decompression neural networks – Motion detection – ATM traffic 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Erol Gelenbe
    • 1
  • Mert Sungur
    • 1
  • Christopher Cramer
    • 1
  • Pamir Gelenbe
    • 1
  1. 1.Department of Electrical Engineering, Duke University, Durham, NC 27708-0291, USAUS

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