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Denoising or compression using wavelet packets

`[xd,treed,perf0,perfl2] = wpdencmp(x,sorh,n,wname,crit,par,keepapp)`

`[___] = wpdencmp(tree,sorh,crit,par,keepapp)`

`[`

returns a denoised or compressed version `xd`

,`treed`

,`perf0`

,`perfl2`

] = wpdencmp(`x`

,`sorh`

,`n`

,`wname`

,`crit`

,`par`

,`keepapp`

)`xd`

of the input data
`x`

obtained by wavelet packet coefficient thresholding.
`wpdencmp`

also returns the wavelet packet best tree
decomposition `treed`

of `xd`

(see `besttree`

for more information),
and the *L*^{2} energy recovery and
compression scores in percentages as `perfl2`

and
`perf0`

, respectively.

[1] Antoniadis, A., and G.
Oppenheim, eds. *Wavelets and Statistics*. Lecture Notes in
Statistics. New York: Springer Verlag, 1995.

[2] Coifman, R. R., and M. V.
Wickerhauser. “Entropy-Based Algorithms for Best Basis Selection.”
*IEEE Transactions on Information Theory*. Vol. 38, Number 2,
1992, pp. 713–718.

[3] DeVore, R. A., B. Jawerth, and
B. J. Lucier. “Image Compression Through Wavelet Transform Coding.”
*IEEE Transactions on Information Theory*. Vol. 38, Number 2,
1992, pp. 719–746.

[4] Donoho, D. L. “Progress
in Wavelet Analysis and WVD: A Ten Minute Tour.” *Progress in Wavelet
Analysis and Applications* (Y. Meyer, and S. Roques, eds.).
Gif-sur-Yvette: Editions Frontières, 1993.

[5] Donoho, D. L., and I. M.
Johnstone. “Ideal Spatial Adaptation by Wavelet Shrinkage.”
*Biometrika*. Vol. 81, 1994, pp. 425–455.

[6] Donoho, D. L., I. M.
Johnstone, G. Kerkyacharian, and D. Picard. “Wavelet Shrinkage:
Asymptopia?” *Journal of the Royal Statistical Society*,
*series B*. Vol. 57, Number 2, 1995,
pp. 301–369.

`besttree`

|`ddencmp`

|`wden`

|`wdencmp`

|`wdenoise`

|`wenergy`

|`wentropy`

|`wpbmpen`

|`wpdec`

|`wpdec2`

|`wthresh`