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Optimal context quantization in lossless compression of image data sequences.

Søren Forchhammer1, Xiaolin Wu, Jakob Dahl Andersen

  • 1Research Center COM, Technical University of Denmark, DK-2800 Lyngby, Denmark. sf@com.dtu.dk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 21, 2004
PubMed
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This study introduces optimal context quantization for image compression, balancing model complexity and data statistics. It presents a dynamic programming algorithm for binary sources and an approach for m-ary sources, improving coding efficiency.

Area of Science:

  • Digital image processing
  • Information theory
  • Data compression

Background:

  • Context-based entropy coding is crucial for image compression performance.
  • A key challenge is balancing model complexity (large templates) with data sparsity (context dilution).

Purpose of the Study:

  • To develop an optimal quantizer for K-dimensional causal contexts in image compression.
  • To minimize static or adaptive code length for improved data representation.

Main Methods:

  • Defined optimality of context quantization as minimum code length.
  • Developed a fast dynamic programming algorithm for optimal context quantizers in binary alphabets.
  • Proposed approximation algorithms and a decomposition method for m-ary alphabets.

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Main Results:

  • An exact optimal context quantizer was computed for binary sources.
  • Approximation solutions and an m-ary source method were proposed.
  • The technique was applied to digital maps and alpha-plane sequences.

Conclusions:

  • The proposed optimal context quantization enhances coding efficiency in image compression.
  • This method provides a lower bound for achievable code length, aiding evaluation of existing techniques.