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Region-based transform coding of multispectral images.

Marco Cagnazzo1, Giovanni Poggi, Luisa Verdoliva

  • 1Dipartimento di Ingegneria Elettronica e delle Telecomunicazioni Università Federico II di Napoli, 21-80125 Napoli, Italy. cagnazzo@unina.it

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 21, 2007
PubMed
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We developed a region-based compression method for multispectral remote-sensing images. This approach offers an insightful and efficient image description, outperforming current techniques.

Area of Science:

  • Remote Sensing
  • Image Processing
  • Data Compression

Background:

  • Multispectral remote-sensing images require efficient compression methods.
  • Existing methods may not fully leverage image structure for selective data access.
  • Region-based image descriptions offer potential for enhanced data mining and retrieval.

Purpose of the Study:

  • To develop an efficient region-based compression scheme for multispectral remote-sensing images.
  • To demonstrate that region-based compression can be effective in a rate-distortion sense.
  • To provide an image description that is both insightful and efficient.

Main Methods:

  • A generic coding scheme based on Bayesian image segmentation and transform coding was defined.
  • Key design choices, including transform type and rate allocation, were optimized through extensive experimentation.

Related Experiment Videos

  • The final scheme incorporates Bayesian segmentation, class-adaptive Karhunen-Loève spectral transform, and shape-adaptive wavelet spatial transform.

    • Main Results:

    • The proposed region-based compression scheme was shown to be effective in a rate-distortion sense.
    • The method provides an insightful image description, enabling selective download and data mining.
    • The optimized scheme outperforms state-of-the-art conventional compression techniques.

    Conclusions:

    • Region-based compression is a viable and efficient strategy for multispectral remote-sensing images.
    • The combination of Bayesian segmentation and adaptive transforms yields superior compression performance.
    • This approach enhances the utility of remote-sensing data for various applications.