• DocumentCode
    1826839
  • Title

    Image data compression using multiple bases representation

  • Author

    Tilki, John F. ; Beex, A. A Louis

  • Author_Institution
    Bradley Dept. of Electr. Eng., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA
  • fYear
    1994
  • fDate
    20-22 Mar 1994
  • Firstpage
    457
  • Lastpage
    461
  • Abstract
    Digitized images contain huge amounts of information which strain, or exceed, the capacity for their real-time processing, storage, and retrieval. Various compression techniques have been developed to reduce the amount of data necessary for representation. The authors report on a hybrid image data compression procedure based on a multiple bases representation. The multiple bases representation technique described utilizes advantages of transform coding, vector quantization, and predictive coding, while aiming to circumvent the associated disadvantages of each. Preliminary results indicate that this procedure can outperform conventional compression methods, and yield high compression ratios while avoiding prohibitive computational complexity
  • Keywords
    encoding; image coding; linear predictive coding; vector quantisation; hybrid image data compression; image data compression; multiple bases representation; predictive coding; transform coding; vector quantization; Capacitive sensors; Computational complexity; Data compression; Image coding; Image retrieval; Image storage; Information retrieval; Predictive coding; Transform coding; Vector quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    System Theory, 1994., Proceedings of the 26th Southeastern Symposium on
  • Conference_Location
    Athens, OH
  • ISSN
    0094-2898
  • Print_ISBN
    0-8186-5320-5
  • Type

    conf

  • DOI
    10.1109/SSST.1994.287833
  • Filename
    287833