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Wavelets in Source Coding, Communications and Networks

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Published by Hindawi Publishing Corporation .
Written in English


Book details:

Edition Notes

ContributionsJames E. Fowler (Editor), Beatrice Pesquet-Popescu (Editor)
The Physical Object
FormatPaperback
Number of Pages232
ID Numbers
Open LibraryOL13165544M
ISBN 109775945801
ISBN 109789775945808

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  The use of wavelets in the broad areas of source coding, communications, and networks is surveyed. Specifically, the impact of wavelets and wavelet theory in image coding, video coding, image interpolation, image-adaptive lifting transforms, multiple-description coding, and joint source-channel coding is overviewed. Recent contributions in these areas arising in subsequent Cited by: BibTeX @MISC{Fowler_waveletsin, author = {James E. Fowler and et al.}, title = {Wavelets in Source Coding, Communications, and Networks: An Overview}, year = {}}. The use of wavelets in the broad areas of source coding, communications, and networks is surveyed. Specifically, the impact of wavelets and wavelet theory in image coding, video coding, image interpolation, image-adaptive lifting transforms, multiple-description coding, and joint source-channel coding is overviewed. The use of wavelets in the broad areas of source coding, communications, and networks is surveyed. Specifically, the impact of wavelets and wavelet theory in image coding, video coding, image interpolation, image-adaptive lifting transforms, multipledescription coding, and joint source-channel coding is overviewed.

Abstract The use of wavelets in the broad areas of source coding, communications, and networks is surveyed. Specifically, the impact of wavelets and wavelet theory in image coding, video coding, image interpolation, image-adaptive lifting transforms, multiple-description coding, and joint source-channel coding is overviewed.   Abstract. Wavelets have been favorably applied in almost all aspects of digital wireless communication systems including data compression, source and channel coding, signal denoising, channel modeling and design of transceivers. The following Matlab project contains the source code and Matlab examples used for discret wavelet transform got daub2. y=d2wavelet(x,Fs,level) does the 2nd order Daubechies Wavelet Transform of signal x with a sampling frequency 'Fs' and the DWT is decomposition is done upto a 'level' It returns the matrix of all decompositions and the final approximations. The following Matlab project contains the source code and Matlab examples used for a wavelet domain non parametric statistical approach for image denoising. This is a demo program of the paper J. Tian, L. Chen and L. Ma, "A wavelet-domain non-parametric statistical approach for image denoising," IEICE Electronics Express, Vol. 7, Sep. , pp.

The subject of wavelets crystallized in the early 90's so this book (published in ) will stay a reference for quite a while. Mallat is one of the main contributors to the theory of wavelets and multiresolution analysis. This book is used as the main reference for the class "Wavelets and modern signal processing" at Caltech. Purchase Wavelets in Image Communication, Volume 5 - 1st Edition. Print Book & E-Book. ISBN , Wavelet Video Coding – realistic channel models for effective joint source/channel coding Possible solution: compression meets the network Do not require the transport mechanism to be flawless (modulation, channel coding, transmission protocol etc.), just design the coding. WAVELETS AND SUBBAND CODING by Martin Vetterli and Jelena Kovačević. First published in , Wavelets and Subband Coding offered a unified view of the exciting field of wavelets and their discrete-time cousins, filter banks, or subband coding. The book developed the theory in both continuous and discrete time, and presented important applications.