• DocumentCode
    2521355
  • Title

    Smooth compression, Gallager bound and nonlinear sparse-graph codes

  • Author

    Montanari, Andrea ; Mossel, Elchanan

  • Author_Institution
    EE & Stat. Depts., Stanford Univ., Stanford, CA
  • fYear
    2008
  • fDate
    6-11 July 2008
  • Firstpage
    2474
  • Lastpage
    2478
  • Abstract
    A data compression scheme is defined to be smooth if its image (the codeword) depends gracefully on the source (the data). Smoothness is a desirable property in many practical contexts, and widely used source coding schemes lack of it. We introduce a family of smooth source codes based on sparse graph constructions, and prove them to achieve the (information theoretic) optimal compression rate for a dense set of iid sources. As a byproduct, we show how Gallager bound on sparsity can be overcome using non-linear function nodes.
  • Keywords
    graph theory; nonlinear codes; smoothing methods; source coding; Gallager bound; data compression; information theory; nonlinear sparse-graph code; optimal compression rate; smooth compression; smooth source codes; source coding; Chaos; Data compression; Error probability; Hamming distance; Image coding; Noise robustness; Random variables; Source coding; Statistics; Temperature sensors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory, 2008. ISIT 2008. IEEE International Symposium on
  • Conference_Location
    Toronto, ON
  • Print_ISBN
    978-1-4244-2256-2
  • Electronic_ISBN
    978-1-4244-2257-9
  • Type

    conf

  • DOI
    10.1109/ISIT.2008.4595436
  • Filename
    4595436