将完美重构滤波器组分解为因果提升矩阵:一种二阶方法

Christopher M. Brislawn
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引用次数: 0

摘要

多项式环上的双变量线性二叉方程的基本理论被用于构建因果双通道 FIR 完美重构传递矩阵和小波变换的因果提升因式分解(基本矩阵分解)。Diophantine 方法生成的因果因数化满足某些多项式程度递减不等式,从而实现了一种名为 "因果补全算法 "的新因数化策略。这为道贝奇斯(Daubechies)和斯韦尔登斯(Sweldens)利用扩展欧几里得算法为劳伦多项式开发的非因果提升方案提供了因果(即多项式,因此可实现)替代方案。新方法用高斯消元法取代欧几里得算法,采用多项式除法的轻微广义化,确保余数满足用户指定的可分性约束的商的存在性和唯一性。因果互补算法比欧几里得算法的因果版本更具通用性,因为它可以生成多项式欧几里得算法所无法获得的因果提升因式。
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Factoring perfect reconstruction filter banks into causal lifting matrices: A Diophantine approach
The elementary theory of bivariate linear Diophantine equations over polynomial rings is used to construct causal lifting factorizations (elementary matrix decompositions) for causal two-channel FIR perfect reconstruction transfer matrices and wavelet transforms. The Diophantine approach generates causal factorizations satisfying certain polynomial degree-reducing inequalities, enabling a new factorization strategy called the Causal Complementation Algorithm. This provides a causal (i.e., polynomial, hence realizable) alternative to the noncausal lifting scheme developed by Daubechies and Sweldens using the Extended Euclidean Algorithm for Laurent polynomials. The new approach replaces the Euclidean Algorithm with Gaussian elimination employing a slight generalization of polynomial division that ensures existence and uniqueness of quotients whose remainders satisfy user-specified divisibility constraints. The Causal Complementation Algorithm is shown to be more general than the causal version of the Euclidean Algorithm approach by generating additional causal lifting factorizations beyond those obtainable using the polynomial Euclidean Algorithm.
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