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Hierarchical segmentation-based image coding using hybrid quad-binary trees.

Ashraf A Kassim1, Wei Siong Lee, Dornoosh Zonoobi

  • 1Electrical and Computer Engineering Department, National University of Singapore, Singapore. ashraf@nus.edu.sg

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 29, 2009
PubMed
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A new image coding method uses a hybrid quad-binary (QB) tree to efficiently represent image geometry. This technique offers superior performance for contour features, particularly at low bit rates.

Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Image approximation and coding are crucial for efficient data transmission and storage.
  • Existing tree-based methods like wedgelets have limitations in representing complex geometrical features.

Purpose of the Study:

  • To introduce a novel segmentation-based image approximation and coding technique.
  • To leverage a hybrid quad-binary (QB) tree structure for enhanced geometrical information modeling.

Main Methods:

  • Development of a hybrid quad-binary (QB) tree structure for image segmentation and representation.
  • Application of the QB-tree to model and code geometrical information, including contours, junctions, and corners.
  • Comparative analysis against existing methods like wedgelets.

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Main Results:

  • The proposed QB-tree method demonstrates greater efficiency in modeling a wide range of contour features.
  • The technique shows particular effectiveness for complex features such as junctions, corners, and ridges.
  • Improved performance is observed, especially at low bit rates compared to traditional methods.

Conclusions:

  • The hybrid quad-binary (QB) tree offers a more efficient approach to image approximation and coding.
  • This method provides a robust solution for representing intricate geometrical details in images.
  • The QB-tree technique is particularly advantageous for applications requiring high compression ratios.