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Related Experiment Videos

Compression of map images by multilayer context tree modeling.

Pavel Kopylov1, Pasi Fränti

  • 1University of Joensuu, FI-80101 Joensuu, Finland. justas@cs.joensuu.fi

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 14, 2005
PubMed
Summary

This study introduces a new image compression method using context tree modeling and arithmetic coding for color map images. The technique significantly improves compression efficiency by leveraging interlayer correlations, outperforming existing standards like JBIG.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Color map images often contain semantic layers and binary layers that can be exploited for compression.
  • Existing compression methods may not fully utilize interlayer correlations in multicomponent images.

Purpose of the Study:

  • To develop an efficient compression method for color map images.
  • To effectively utilize interlayer correlations between image layers.

Main Methods:

  • Context tree modeling and arithmetic coding applied to separated image layers.
  • Optimizing context trees for inter-layer dependencies.
  • Solving the layer ordering problem using a directed spanning tree algorithm (Edmond's algorithm).
  • Optimal selection and removal of background color.

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Main Results:

  • Achieved 50% better compression than JBIG.
  • Achieved 25% better compression than single-layer context tree modeling.
  • Demonstrated effective utilization of interlayer correlations.

Conclusions:

  • The proposed method offers superior compression performance for color map images.
  • Leveraging interlayer dependencies through optimized context trees is key to improved compression.