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Related Experiment Videos

A new class of biorthogonal wavelet systems for image transform coding.

D Wei1, J Tian, R R Wells

  • 1Dept. of Electr. and Comput. Eng., Rice Univ., Houston, TX 77251-1892, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 16, 2008
PubMed
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New biorthogonal Coifman wavelet systems offer competitive compression and lower computational complexity. These systems provide an improved tradeoff for wavelet transform coding applications.

Area of Science:

  • Signal Processing
  • Applied Mathematics
  • Image Compression

Background:

  • Biorthogonal wavelets are crucial for signal and image compression.
  • Existing wavelet systems like the 9-7 tap filterbank have limitations in computational complexity.
  • The need for efficient and effective wavelet transforms persists in various applications.

Purpose of the Study:

  • To introduce a novel class of biorthogonal Coifman wavelet systems.
  • To develop a time-domain design method for these systems.
  • To evaluate their compression potential and computational efficiency.

Main Methods:

  • Construction of general biorthogonal Coifman wavelet systems with distributed vanishing moments.
  • Derivation of closed-form expressions for dual filter impulse and frequency responses.

Related Experiment Videos

  • Analysis of filter coefficients for multiplication-free discrete wavelet transform realization.

    • Main Results:

    • The proposed systems feature dyadic fraction filter coefficients, enabling multiplication-free transforms.
    • Even-ordered systems exhibit symmetry, resulting in linear-phase dual filters.
    • Three specific filterbanks demonstrate compression potential comparable to the 9-7 tap system.

    Conclusions:

    • The new biorthogonal Coifman wavelet systems offer a superior tradeoff between compression performance and computational complexity.
    • Their design facilitates efficient implementation in discrete wavelet transform.
    • These systems represent a promising advancement for wavelet transform coding.