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Image coding with geometric wavelets.

Dror Alani1, Amir Averbuch, Shai Dekel

  • 1School of Computer Science, Tel Aviv University, Israel. alanid@zahav.net.il

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 8, 2007
PubMed
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This study introduces a new image coding method using geometric wavelets (GW) for efficient, sparse image representation. The GW approach offers improved performance over existing wavelet and sparse geometric methods at low bit rates.

Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Efficient image coding is crucial for data compression.
  • Existing wavelet-based methods like EZW, SPIHT, and EBCOT have limitations in capturing complex image features.
  • Sparse geometric representation methods show promise but require further optimization.

Purpose of the Study:

  • To develop a novel and efficient low bit-rate image coding method.
  • To leverage multivariate nonlinear piecewise polynomial approximation for enhanced image representation.
  • To improve upon the performance of state-of-the-art image compression algorithms.

Main Methods:

  • The proposed method combines a binary space partition scheme with geometric wavelet (GW) tree approximation.

Related Experiment Videos

  • This approach efficiently captures curve singularities and achieves sparse image representation.
  • Computational intensity is addressed by aggregating global GW n-term approximations from tiled image segments.

    • Main Results:

    • The geometric wavelet (GW) method demonstrates competitive performance against leading algorithms.
    • Achieved approximately 0.4 dB gain over SPIHT and EBCOT at 0.0625 bits-per-pixel (bpp).
    • Outperformed Bandelets algorithm by 0.27 dB at 0.1 bpp, indicating superior sparse geometric representation.

    Conclusions:

    • The novel geometric wavelet (GW) image coding method provides significant efficiency gains at low bit rates.
    • This technique effectively represents image features, particularly curve singularities, leading to superior compression ratios.
    • The method shows strong potential for advancing the field of image compression technology.