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Morphological operators for image and video compression.

P Salembier1, P Brigger, J R Casas

  • 1Dept. of Signal Theory and Commun., Univ. Politecnica de Catalunya, Barcelona.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
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This study explores mathematical morphology tools for efficient image and video compression. These shape-based methods offer powerful techniques for segmentation and coding, enhancing data compression effectiveness.

Area of Science:

  • Digital Image Processing
  • Signal Processing
  • Computer Vision

Background:

  • Mathematical morphology offers a shape-oriented approach to signal processing.
  • Its unique features are highly beneficial for data compression applications.
  • Existing methods can be enhanced by incorporating morphological principles.

Purpose of the Study:

  • To present and discuss morphological tools applicable to image and video compression.
  • To highlight the utility of these tools beyond standard coding algorithms.
  • To demonstrate their effectiveness in segmentation and coding tasks.

Main Methods:

  • Connected operators for image analysis.
  • Region-growing watershed algorithm for segmentation.
  • Geodesic skeletonization for feature extraction.

Related Experiment Videos

  • Morphological interpolation for data reconstruction.

    • Main Results:

    • Demonstrated implementation of four key morphological transformations.
    • Showcased applications in image and video segmentation.
    • Illustrated utility for contour and texture coding.
    • Provided a foundation for developing novel compression techniques.

    Conclusions:

    • Morphological tools provide effective solutions for image and video compression.
    • These methods facilitate advanced segmentation, contour, and texture coding.
    • The described techniques offer a versatile framework for signal processing and compression.