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Lossless image compression with multiscale segmentation.

Krishna Ratakonda1, Narendra Ahuja

  • 1Multimedia Department, IBM Research, Yorktown Heights, NY 10598, USA. ratakond@us.ibm.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 6, 2008
PubMed
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This study introduces a novel lossless image compression method using optimal segmentation. The technique achieves 15-20% better compression than JPEG lossless by balancing segmentation data coding and spatial redundancy exploitation.

Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Spatial redundancies in image data limit lossless compression efficiency.
  • Existing methods may not optimally balance segmentation overhead and coding gain.

Purpose of the Study:

  • Develop a lossless image compression method utilizing optimal segmentation information.
  • Improve compression ratios compared to current standards like JPEG lossless.

Main Methods:

  • Employing a previously proposed transform for multiscale, tree-structured image segmentation.
  • Pruning the segmentation tree to control region size and number for rate-optimal balance.
  • Utilizing an image model with distinct descriptions for edge and interior pixels.

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Main Results:

  • The proposed algorithm achieves performance comparable to state-of-the-art methods.
  • Demonstrates 15-20% improved compression over JPEG lossless for diverse image types.

Conclusions:

  • The novel approach effectively exploits spatial redundancies through optimized segmentation.
  • The method offers a superior lossless image compression solution, outperforming JPEG lossless.