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Inverse and Parseval identity for a periodic orthogonal compact-filter transform
Statement
For every admissible pair , every even , and every , the analysis matrix in Periodic finite orthogonal compact-filter wavelet transform is orthogonal. Thus synthesis is its inverse and . Every multilevel transform defined there is also orthogonal, with inverse given by reversing the stages and applying each stage's transpose.
Facts & Assumptions
Given: An admissible real filter pair of length , an even length , and .
Periodic finite orthogonal compact-filter wavelet transform: row of the low-pass or high-pass block has its nonzero tap at coordinate ; the tap correlation at every even shift is the stated Kronecker delta or zero.
The Euclidean inner product on : row inner products are finite sums of coordinate products.
Proof
Fix row types and row indices . A coordinate can contribute to both rows only when for taps . Hence the row inner product is the sum of over such pairs. The difference lies in , whose width is ; so at most one representative of the congruence class modulo occurs. Since and are even, that representative, when present, is for an integer . The inner product is therefore the filter correlation , which equals exactly for the same filter and , and equals otherwise by [F1]. In the same-filter case forces , because lie in a complete residue system modulo . Thus the rows are orthonormal.
A square real matrix with orthonormal rows satisfies ; hence it is invertible with inverse , so also . It follows that . A multilevel transform is a composition of these one-level orthogonal maps and coordinate permutations; composing their transposes in reverse order gives its inverse and preserves norm.
Depends on
Used by
Dependency tree · two levels
15 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Gilbert Strang and Kevin Amaratunga, Wavelets and Filter Banks, MIT OpenCourseWare 18.327, Handouts 3 and 7 (standard reference, not scraped)
- Stéphane Mallat, A Wavelet Tour of Signal Processing, 3rd ed., Chapter 9 (standard reference, not scraped)