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Adaptive non-linear predictor for lossless image compression

  • Václav Hlaváč
  • Jaroslav Fojtík
Low Level Processing II
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1296)

Abstract

The paper proposes the new method for lossless image compression that performs wery well and results can be compared with other methods that we are aware of. We developed further the Slessinger's idea to represent an image as residuals of a special local predictor. The predictor configurations in a binary image are grouped into couples that differ in representative point only. Only residuals that correspond to the less frequent predictor from the couple is stored. An optimal predictor is based on the frequency of predictor configuration in the image. Two main extensions are proposed. (1) The method is generalized for grey-level image or images with even more degrees of freedom. (2) The method that works with addaptive estimator is proposed. The resulting FH-Adapt algorithm performs very well and results could be compared with most of the current algorithms for the same purpose. The predictor can learn automatically from the compressed image and cope even with complicated fine textures. We are able to estimate the influence of the cells in a neighbourhood and use only significant ones for compression.

Keywords

Binary Image Frequency Table Huffman Code Technical Drawing Optimal Predictor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Václav Hlaváč
    • 1
  • Jaroslav Fojtík
    • 1
  1. 1.Faculty of Electrical Engineering Center for Machine PerceptionCzech Technical UniversityPrague 2Czech Republic

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