The Determination of Critical-Sampling Scheme of Preprocessing for Multiwavelets Decomposition as 1st and 2nd Orders of Approximations.

One of the important differences between multiwavelets and scalar wavelets is that each channel in the filter bank has a vector-valued input and a vector-valued output. A scalar-valued input signal must somehow be converted into a suitable vector-valued signal. This conversion is called preprocessi...

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Autores principales: W. A. Mahmoud, Z. J. M. Saleh, N. K. Wafi
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Publicado: Al-Khwarizmi College of Engineering – University of Baghdad 2017
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spelling oai:doaj.org-article:ab181ce6b3f84009a84e14f9e3523f012021-12-02T02:33:59ZThe Determination of Critical-Sampling Scheme of Preprocessing for Multiwavelets Decomposition as 1st and 2nd Orders of Approximations.1818-11712312-0789https://doaj.org/article/ab181ce6b3f84009a84e14f9e3523f012017-12-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/18https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 One of the important differences between multiwavelets and scalar wavelets is that each channel in the filter bank has a vector-valued input and a vector-valued output. A scalar-valued input signal must somehow be converted into a suitable vector-valued signal. This conversion is called preprocessing. Preprocessing is a mapping process which is done by a prefilter. A postfilter just does the opposite. The most obvious way to get two input rows from a given signal is to repeat the signal. Two rows go into the multifilter bank. This procedure is called “Repeated Row” which introduces oversampling of the data by a factor of 2.  For data compression, where one is trying to find compact transform representations for a dataset, it is imperative to find critically sampled multiwavelet transforms schemes which this paper focuses on finding a simple and easy to follow algorithm for its computation. One famous multiwavelet filter used here is the GHM filter proposed by Geronimo, Hardian, and Massopust. The GHM basis offers a combination of orthogonality, symmetry, and compact support, which can not be achieved by any scalar wavelet basis. Using a computer program for the proposed method, an example test on Lena image is verified which shows image properties after a single level decomposition and the reconstructed image after reconstruction.   W. A. MahmoudZ. J. M. SalehN. K. WafiAl-Khwarizmi College of Engineering – University of BaghdadarticleKeyword: Discrete Multiwavelete Transform (DMWT), Inverse Discrete Multiwavelete      Transform (IDMWT), Critical-Sampling, Schema of Processing.Chemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 1, Iss 1 (2017)
institution DOAJ
collection DOAJ
language EN
topic Keyword: Discrete Multiwavelete Transform (DMWT), Inverse Discrete Multiwavelete      Transform (IDMWT), Critical-Sampling, Schema of Processing.
Chemical engineering
TP155-156
Engineering (General). Civil engineering (General)
TA1-2040
spellingShingle Keyword: Discrete Multiwavelete Transform (DMWT), Inverse Discrete Multiwavelete      Transform (IDMWT), Critical-Sampling, Schema of Processing.
Chemical engineering
TP155-156
Engineering (General). Civil engineering (General)
TA1-2040
W. A. Mahmoud
Z. J. M. Saleh
N. K. Wafi
The Determination of Critical-Sampling Scheme of Preprocessing for Multiwavelets Decomposition as 1st and 2nd Orders of Approximations.
description One of the important differences between multiwavelets and scalar wavelets is that each channel in the filter bank has a vector-valued input and a vector-valued output. A scalar-valued input signal must somehow be converted into a suitable vector-valued signal. This conversion is called preprocessing. Preprocessing is a mapping process which is done by a prefilter. A postfilter just does the opposite. The most obvious way to get two input rows from a given signal is to repeat the signal. Two rows go into the multifilter bank. This procedure is called “Repeated Row” which introduces oversampling of the data by a factor of 2.  For data compression, where one is trying to find compact transform representations for a dataset, it is imperative to find critically sampled multiwavelet transforms schemes which this paper focuses on finding a simple and easy to follow algorithm for its computation. One famous multiwavelet filter used here is the GHM filter proposed by Geronimo, Hardian, and Massopust. The GHM basis offers a combination of orthogonality, symmetry, and compact support, which can not be achieved by any scalar wavelet basis. Using a computer program for the proposed method, an example test on Lena image is verified which shows image properties after a single level decomposition and the reconstructed image after reconstruction.  
format article
author W. A. Mahmoud
Z. J. M. Saleh
N. K. Wafi
author_facet W. A. Mahmoud
Z. J. M. Saleh
N. K. Wafi
author_sort W. A. Mahmoud
title The Determination of Critical-Sampling Scheme of Preprocessing for Multiwavelets Decomposition as 1st and 2nd Orders of Approximations.
title_short The Determination of Critical-Sampling Scheme of Preprocessing for Multiwavelets Decomposition as 1st and 2nd Orders of Approximations.
title_full The Determination of Critical-Sampling Scheme of Preprocessing for Multiwavelets Decomposition as 1st and 2nd Orders of Approximations.
title_fullStr The Determination of Critical-Sampling Scheme of Preprocessing for Multiwavelets Decomposition as 1st and 2nd Orders of Approximations.
title_full_unstemmed The Determination of Critical-Sampling Scheme of Preprocessing for Multiwavelets Decomposition as 1st and 2nd Orders of Approximations.
title_sort determination of critical-sampling scheme of preprocessing for multiwavelets decomposition as 1st and 2nd orders of approximations.
publisher Al-Khwarizmi College of Engineering – University of Baghdad
publishDate 2017
url https://doaj.org/article/ab181ce6b3f84009a84e14f9e3523f01
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