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Related Experiment Videos

A progressive transmission image coder using linear phase uniform filterbanks as block transforms.

T D Tran1, T Q Nguyen

  • 1Dept. of Electr. and Comput. Eng., Wisconsin Univ., Madison, WI 53706, USA. ttran@ece.jhu.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
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A new image coding scheme enhances compression by replacing the standard wavelet transform with M-channel filterbanks. This novel approach improves energy compaction and outperforms existing methods for various image types.

Area of Science:

  • Digital Signal Processing
  • Image Compression
  • Computer Vision

Background:

  • Existing image coding schemes often use dyadic wavelet transforms.
  • Embedded Zerotree Wavelet (EZW) framework offers progressive transmission and embedded quantization.
  • Limitations exist in frequency partitioning and energy compaction of standard transforms.

Purpose of the Study:

  • To introduce a novel image coding scheme.
  • To improve upon the EZW framework using advanced filterbanks.
  • To achieve superior image compression performance.

Main Methods:

  • Replaced the dyadic wavelet transform in the EZW framework with M-channel uniform-band maximally decimated linear phase perfect reconstruction filterbanks (LPPRFBs).
  • Implemented the transform stage as a block transform supporting parallel processing.

Related Experiment Videos

  • Utilized efficient lattice structures for hardware implementation.

    • Main Results:

    • Achieved finer frequency spectrum partitioning and higher energy compaction.
    • The new coder retains EZW's attractive properties like progressive transmission and exact bit rate control.
    • Outperformed previously published image coders on natural, hard-to-code images across various bit rates.

    Conclusions:

    • The proposed M-channel LPPRFBs integrated into the EZW framework offer a significant advancement in image coding.
    • The scheme provides enhanced compression efficiency and retains desirable features for practical applications.
    • This novel approach demonstrates superior performance, particularly for complex image datasets.