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A segmentation-based predictive multiresolution image coder.

X Wu1, Y Fang

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

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study introduces adaptive, tree-structured segmentation for image compression, improving multiresolution coding by reflecting image structures. This novel approach offers subjectively appealing results and efficient processing for predictive image coders.

Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Recursive rectilinear tessellations, such as quadtrees, are common in image coding but lack flexibility.
  • Rigid tessellations struggle to adapt to complex image structures, limiting compression efficiency.
  • Existing methods may not optimally capture the scene's inherent complexity.

Purpose of the Study:

  • To develop a novel image pyramid framework for predictive multiresolution image coding.
  • To enhance image compression by utilizing adaptive, tree-structured segmentation.
  • To improve the reflection of image scene structures in compression algorithms.

Main Methods:

  • Image pyramid construction via adaptive, tree-structured segmentation.
  • Recursive, least-squares piecewise functional approximation for coder construction.

Related Experiment Videos

  • Incremental least-squares computation for efficient encoding.

    • Main Results:

    • Subjectively appealing compression results achieved at various resolutions.
    • Effective exploitation of statistical dependencies between image pyramid layers.
    • Demonstration of an efficient encoding process using incremental computation.

    Conclusions:

    • Adaptive segmentation offers a more suitable approach than rigid tessellations for image compression.
    • The proposed predictive multiresolution image coder achieves high-quality results.
    • Efficient encoding and simple decoding facilitate practical real-time applications.