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Region-based fractal image compression using heuristic search.

L Thomas1, F Deravi

  • 1Dept. of Electr. and Electron. Eng., Univ. Coll. of Wales, Swansea.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study introduces fractal image compression using a novel region-based partitioning method. This approach significantly improves compression ratios compared to traditional block-based methods, achieving high quality at a 41:1 ratio.

Area of Science:

  • Computer Science
  • Image Processing
  • Data Compression

Background:

  • Image redundancy is a key challenge in efficient data storage and transmission.
  • Traditional block-based partitioning methods in fractal image compression have limitations in achieving high compression ratios.
  • Self-transformability offers a potential avenue for exploiting image redundancy.

Purpose of the Study:

  • To develop and evaluate a novel region-based fractal image compression algorithm.
  • To compare the performance of the region-based approach against traditional block-based methods.
  • To investigate the effectiveness of heuristic algorithms in constructing region-based transformations.

Main Methods:

  • Fractal (or attractor) image compression utilizing self-transformability.

Related Experiment Videos

  • Novel region-based image partitioning instead of traditional block-based partitioning.
  • Heuristic algorithms employed for constructing region-based transformations due to large search spaces.

    • Main Results:

    • The region-based system achieved nearly double the compression ratio of the block-based system.
    • Similar decompressed image quality was maintained between the two methods.
    • A compression ratio of 41:1 was achieved for the Lena image at a PSNR of 26.56 dB.

    Conclusions:

    • Region-based partitioning significantly enhances fractal image compression performance.
    • Heuristic algorithms are effective in managing the complexity of region-based transformations.
    • The proposed method offers a substantial improvement in compression efficiency for digital images.