• David S. Taubman
  • Michael W. Marcellin
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 642)


Chapter 2 discussed entropy coding algorithms possessing the desirable feature that the data obtained from decompression are identical to the original data. That is, the compression algorithms described in that chapter are lossless. As mentioned in Chapter 1, some applications (such as certain medical imaging systems) require lossless compression, while other applications may tolerate some amount of distortion in the decompressed data in return for a smaller compressed representation. Quantization is the element of lossy compression systems responsible for reducing the precision of data in order to make them more compressible. In most lossy compression systems, it is the only source of distortion.


Vector Quantization Viterbi Algorithm Probability Density Function Scalar Quantization Voronoi Region 
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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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • David S. Taubman
    • 1
  • Michael W. Marcellin
    • 2
  1. 1.Electrical Engineering and TelecommunicationsThe University of New South WalesSydneyAustralia
  2. 2.Electrical and Computer EngineeringThe University of ArizonaTucsonUSA

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