Multiresolution Decomposition

  • Friedrich O. Huck
  • Carl L. Fales
  • Zia-ur Rahman
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 409)


This chapter includes multiresolution decomposition for image analysis and data compression. Multiresolution processing has been implemented with many different architectures (tree structures) and filters (operators) for signal decomposition (analysis) and reconstruction (synthesis).1–13 Therefore, Section 5.1 begins with a single-level decomposition that most architectures share, and Section 5.2 extends the formulations to a particular multi-level realization, the wavelet transform. Finally, Section 5.3 characterizes the performance of this decomposition in the visual communication channel. This characterization focuses on the effects of the quantization of the wavelet transform coefficients (or requantization) on the information rate, data rate and image quality.


Spatial Frequency Discrete Wavelet Transform Image Restoration Information Rate Visual Communication 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Friedrich O. Huck
    • 1
  • Carl L. Fales
    • 1
  • Zia-ur Rahman
    • 2
  1. 1.Research and Technology GroupNASA Langley Research CenterUSA
  2. 2.Department of Computer ScienceCollege of William & MaryUSA

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