• DocumentCode
    2685061
  • Title

    Adaptive linear prediction lossless image coding

  • Author

    Motta, Giovanni ; Storer, James A. ; Carpentieri, Bruno

  • Author_Institution
    Dept. of Comput. Sci., Brandeis Univ., Waltham, MA, USA
  • fYear
    1999
  • fDate
    29-31 Mar 1999
  • Firstpage
    491
  • Lastpage
    500
  • Abstract
    The practical lossless digital image compressors that achieve the best results in terms of compression ratio are also simple and fast algorithms with low complexity both in terms of memory usage and running time. Surprisingly, the compression ratio achieved by these systems cannot be substantially improved even by using image-by-image optimization techniques or more sophisticate and complex algorithms. Meyer and Tischer (1998) were able, with their TMW, to improve some current best results (they do not report results for all test images) by using global optimization techniques and multiple blended linear predictors. Our investigation is directed to determine the effectiveness of an algorithm that uses multiple adaptive linear predictors, locally optimized on a pixel-by-pixel basis. The results we obtained on a test set of nine standard images are encouraging, where we improve over CALIC on some images
  • Keywords
    adaptive codes; computational complexity; data compression; image coding; linear predictive coding; optimisation; complexity; compression ratio; digital image compressors; local optimization; lossless image coding; multiple adaptive linear prediction; Compressors; Computational complexity; Digital images; Entropy; IEC standards; ISO; Image coding; Performance loss; Pixel; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Compression Conference, 1999. Proceedings. DCC '99
  • Conference_Location
    Snowbird, UT
  • ISSN
    1068-0314
  • Print_ISBN
    0-7695-0096-X
  • Type

    conf

  • DOI
    10.1109/DCC.1999.755699
  • Filename
    755699