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Context-based lossless interband compression--extending CALIC.

X Wu1, N Memon

  • 1Dept. of Comput. Sci., Univ. of Western Ontario, London, Ont., Canada.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 8, 2008
PubMed
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This study introduces an enhanced lossless image compression method, interband CALIC, for multispectral images. It achieves significant bit rate reductions compared to existing techniques with minimal computational overhead.

Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Context-based, adaptive, lossless image codec (CALIC) is a leading lossless image compression technique.
  • Multispectral images require specialized interband coding for efficient compression.
  • Existing methods often fail to exploit higher-order interband correlations effectively.

Purpose of the Study:

  • To develop an interband version of CALIC for improved multispectral image compression.
  • To leverage CALIC's context modeling for interband correlation exploitation.
  • To achieve significant compression gains over intraband methods.

Main Methods:

  • Proposed an interband CALIC algorithm building upon the original CALIC framework.
  • Integrated context modeling of DPCM errors to capture interband redundancies.

Related Experiment Videos

  • Exploited both interband and intraband statistical redundancies for compression.

    • Main Results:

    • The interband CALIC significantly outperforms its intraband counterpart.
    • Achieved bit rate reductions exceeding 20% on certain multispectral images.
    • Demonstrated effective exploitation of higher-order interband correlations.

    Conclusions:

    • Interband CALIC offers substantial compression gains for multispectral images.
    • The method effectively models interband correlations beyond simple linear predictors.
    • This approach provides a practical and efficient solution for lossless multispectral image compression with modest computational increase.