The Binomial Qmf-Wavelet Transform for Multiresolution Signal Decomposition

Ali N. Akansu; Richard A. Haddad; Hakan Caglar

doi:10.1109/TSP.1993.193123

The Binomial Qmf-Wavelet Transform for Multiresolution Signal Decomposition

Ali N. Akansu, Richard A. Haddad, Hakan Caglar

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

36 Scopus citations

Abstract

This paper describes a class of orthogonal binomial filters that provide a set of basis functions for a bank of perfect reconstruction (PR) finite impulse response quadrature mirror filters (FIR QMF). These binomial QMF’s are shown to be the same filters as those derived from a discrete orthonormal wavelet transform approach by Daubechies. These filters are the unique maximally flat magnitude square PR QMF's. It is shown that the binomial QMF outperforms the discrete cosine transform objectively for AR(1) sources and test images considered.

Original language	English (US)
Pages (from-to)	13-19
Number of pages	7
Journal	IEEE Transactions on Signal Processing
Volume	41
Issue number	1
DOIs	https://doi.org/10.1109/TSP.1993.193123
State	Published - Jan 1993

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering

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AB - This paper describes a class of orthogonal binomial filters that provide a set of basis functions for a bank of perfect reconstruction (PR) finite impulse response quadrature mirror filters (FIR QMF). These binomial QMF’s are shown to be the same filters as those derived from a discrete orthonormal wavelet transform approach by Daubechies. These filters are the unique maximally flat magnitude square PR QMF's. It is shown that the binomial QMF outperforms the discrete cosine transform objectively for AR(1) sources and test images considered.

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The Binomial Qmf-Wavelet Transform for Multiresolution Signal Decomposition

Abstract

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