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An efficient rate-distortion optimal shape coding approach utilizing a skeleton-based decomposition.

Haohong Wang1, Guido M Schuster, Aggelos K Katsaggelos

  • 1Dept. of Electr. and Comput. Eng., Northwestern Univ., Evanston, IL 60208, USA. haohong@ece.northwestern.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
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This study introduces a novel shape-coding method that separates shape data into skeleton and boundary distance signals. This allows for a flexible balance between accuracy and data size, outperforming existing methods.

Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Traditional shape-coding methods face limitations in balancing approximation error and bit budget.
  • Decoupling shape information is crucial for efficient data representation.

Purpose of the Study:

  • To present a new shape-coding approach that decomposes shape information into skeleton and boundary distance signals.
  • To develop an optimal rate-distortion approach for minimizing overall distortion within a given bit budget.
  • To propose and compare efficient algorithms for solving the shape-coding optimization problem.

Main Methods:

  • Shape information is decoupled into skeleton and boundary distance signal data sets.
  • An operational rate-distortion (ORD) optimal approach using Lagrangian relaxation and a 4D DAG shortest path algorithm is developed.

Related Experiment Videos

  • Suboptimal greedy-trellis search and a relaxed distortion criterion algorithm are proposed to reduce computational complexity.

    • Main Results:

    • The proposed shape-coding approach allows for a more flexible tradeoff between approximation error and bit budget.
    • The optimal ORD approach effectively minimizes overall distortion by optimizing control point selection.
    • Experimental results show that the proposed methods outperform existing ORD optimal approaches.

    Conclusions:

    • The novel shape-coding approach offers significant advantages in flexibility and efficiency.
    • The developed algorithms provide effective solutions for optimizing shape representation under bit constraints.
    • This work advances the field of shape coding through innovative data decomposition and optimization techniques.