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Fractal image compression with region-based functionality.

Kamel Belloulata1, Janusz Konrad

  • 1Département de Génie Electrique et de Génie Informatique, Université de Sherbrooke, Sherbrooke, QC, Canada. kamel.belloulata@courrier.usherb.ca

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
PubMed
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This study introduces a novel fractal image coding method that processes images region by region using segmentation maps. This approach enhances object-based queries and improves subjective image quality while increasing coding speed.

Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Fractal coding is a popular still-image compression technique.
  • Traditional fractal coding uses rectangular blocks, limiting region-based functionalities.
  • Existing methods for irregular region merging lack semantic meaningfulness.

Purpose of the Study:

  • To develop a region-based fractal image coding method for enhanced object-based queries.
  • To enable semantically meaningful region processing in fractal image compression.
  • To improve the efficiency and subjective quality of fractal image coding.

Main Methods:

  • Images are compressed region by region based on a pre-computed segmentation map.
  • Rectangular range and domain blocks are used, with boundary blocks segmented.

Related Experiment Videos

  • A novel dissimilarity measure adapted to segment shape is proposed.
  • Two approaches are presented: spatial domain and transform domain.

    • Main Results:

    • The proposed methods offer region-based functionalities for applications like object-based database queries.
    • Performance in terms of Peak Signal-to-Noise Ratio (PSNR) is comparable to existing methods.
    • Subjective image quality is often improved compared to other tested methods.
    • The methods are faster than other fractal coding techniques due to a limited domain-block codebook size.

    Conclusions:

    • The new region-based fractal image coding approach provides significant advantages for image database applications.
    • The methods demonstrate potential for internet and multimedia applications requiring object-based functionalities.
    • The approach balances functionality, performance, and speed, offering a promising alternative to traditional fractal coding.