On compact wavelet matrices of rank m and of order and degree N
arXiv:1109.380913 citationsh-index: 10
Originality Incremental advance
AI Analysis
Provides a novel theoretical framework for constructing compact wavelet matrices, but the impact is limited to the wavelet signal processing domain.
The paper proposes a new parametrization of compact wavelet matrices of rank m and order/degree N using coordinates in Euclidean space, enabling efficient construction via Wiener-Hopf factorization.
A new parametrization (one-to-one onto map) of compact wavelet matrices of rank $m$ and of order and degree $N$ is proposed in terms of coordinates in the Euclidian space $R^{(m-1)N}$. The developed method depends on Wiener-Hopf factorization of corresponding unitary matrix functions and allows to construct compact wavelet matrices efficiently. Some applications of the proposed method are discussed.