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Fractal image compression based on Delaunay triangulation and vector quantization.

F Davoine1, M Antonini, J M Chassery

  • 1TIMC-IMAG, Inst. Albert Bonniot, La Tronche.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
Summary
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This study introduces adaptive Delaunay triangulation for fractal image compression, enhancing efficiency with vector quantization. The novel approach achieves good compression ratios and reduces encoding complexity.

Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Fractal image compression offers high compression ratios but often suffers from high encoding complexity.
  • Existing methods may lack flexibility in partitioning image data for optimal compression.

Purpose of the Study:

  • To develop a novel fractal image compression scheme using adaptive Delaunay triangulation.
  • To reduce encoding complexity through an integrated vector quantization classification step.

Main Methods:

  • Adaptive Delaunay triangulation is employed for image partitioning based on grey-level information.
  • A modified Lloyd algorithm (vector quantization) is applied to pixel histograms derived from the triangulation.
  • The method retains representative triangles in domain blocks to minimize comparisons.

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Main Results:

  • The proposed method achieves good compression ratios, ranging from 0.25-0.5 bits per pixel.
  • Encoding complexity is significantly reduced due to the vector quantization integration.
  • Compression performance is dependent on image characteristics and the number of retained triangles.

Conclusions:

  • Adaptive Delaunay triangulation provides a flexible and effective partitioning strategy for fractal image compression.
  • Vector quantization successfully reduces computational load without compromising compression quality.
  • This integrated approach offers an efficient solution for high-quality fractal image compression.