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About Wavelet Packet Analysis

Wavelet Toolbox™ software contains functions that let you

  • Examine and explore characteristics of individual wavelet packets

  • Perform wavelet packet analysis of 1-D and 2-D data

  • Use wavelet packets to compress and remove noise from signals and images

For more background on the wavelet packets, see the section Wavelet Packets.

Some object-oriented programming features are used for wavelet packet tree structures. For more detail, refer to Introduction to Object-Oriented Features.

This chapter takes you through the features of 1-D and 2-D wavelet packet analysis using the Wavelet Toolbox software. You'll learn how to

  • Load a signal or image

  • Perform a wavelet packet analysis of a signal or image

  • Compress a signal

  • Remove noise from a signal

  • Compress an image

  • Show statistics and histograms

The toolbox provides these functions for wavelet packet analysis. For more information, see the reference pages. The reference entries for these functions include examples showing how to perform wavelet packet analysis via the command line.

More examples can be found in the section Examples Using Wavelet Packet Tree Objects.

Analysis-Decomposition Functions

Function Name



Wavelet packet coefficients

wpdec and wpdec2

Full decomposition


Decompose packet

Synthesis-Reconstruction Functions

Function Name



Reconstruct coefficients

wprec and wprec2

Full reconstruction


Recompose packet

Decomposition Structure Utilities

Function Name



Find best tree


Find best level tree


Update wavelet packets entropy


Get WPTREE object fields contents


Read values in WPTREE object fields




Extract wavelet tree from wavelet packet tree


Cut wavelet packet tree

Denoising and Compression

Function Name



Default values for denoising and compression


Penalized threshold for wavelet packet denoising


Denoising and compression using wavelet packets


Wavelet packets coefficients thresholding


Threshold settings manager

In the wavelet packet framework, compression and denoising ideas are exactly the same as those developed in the wavelet framework. The only difference is that wavelet packets offer a more complex and flexible analysis, because in wavelet packet analysis, the details as well as the approximations are split.

A single wavelet packet decomposition gives a lot of bases from which you can look for the best representation with respect to a design objective. This can be done by finding the “best tree” based on an entropy criterion.

Denoising and compression are interesting applications of wavelet packet analysis. The wavelet packet denoising or compression procedure involves four steps:

  1. Decomposition

    For a given wavelet, compute the wavelet packet decomposition of signal x at level N.

  2. Computation of the best tree

    For a given entropy, compute the optimal wavelet packet tree. Of course, this step is optional. The graphical tools provide a Best Tree button for making this computation quick and easy.

  3. Thresholding of wavelet packet coefficients

    For each packet (except for the approximation), select a threshold and apply thresholding to coefficients.

  4. Reconstruction

    Compute wavelet packet reconstruction based on the original approximation coefficients at level N and the modified coefficients.