@inproceedings{908a000d500a4231a29f89c28c8463dc,
title = "Wavelet folding and decorrelation across the scale",
abstract = "The discrete wavelet transform (DWT) gives a compact multiscale representation of signals and provides a hierarchical structure for signal processing. It has been assumed the DWT can fairly well decorrelate real-world signals. However a residual dependency structure still remains between wavelet coefficients. It has been observed magnitudes of wavelet coefficients are highly correlated, both across the scale and at neighboring spatial locations. In this paper we present a wavelet folding technique, which folds wavelet coefficients across the scale and removes the across-The-scale dependence to a larger extent. It produces an even more compact signal representation and the energy is more concentrated in a few large coefficients. It has a great potential in applications such as image compression.",
author = "J. Tian and Baraniuk, {R. G.} and Wells, {R. O.} and Tan, {D. M.} and Wu, {H. R.}",
note = "Publisher Copyright: {\textcopyright} 2000 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000 ; Conference date: 05-06-2000 Through 09-06-2000",
year = "2000",
doi = "10.1109/ICASSP.2000.862039",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "544--547",
booktitle = "Signal Processing Theory and Methods I",
address = "United States",
}