DocumentCode :
1756678
Title :
Lattice Structure for Generalized-Support Multidimensional Linear Phase Perfect Reconstruction Filter Bank
Author :
Xieping Gao ; Bodong Li ; Fen Xiao
Author_Institution :
MOE Key Lab. of Intell. Comput. & Inf. Process. & the Coll. of Inf. Eng., Xiangtan Univ., Xiangtan, China
Volume :
22
Issue :
12
fYear :
2013
fDate :
Dec. 2013
Firstpage :
4853
Lastpage :
4864
Abstract :
Multidimensional linear phase perfect reconstruction filter bank (MDLPPRFB) can be designed and implemented via lattice structure. The lattice structure for the MDLPPRFB with filter support N(MΞ) has been published by Muramatsu , where M is the decimation matrix, Ξ is a positive integer diagonal matrix, and N(N) denotes the set of integer vectors in the fundamental parallelepiped of the matrix N. Obviously, if Ξ is chosen to be other positive diagonal matrices instead of only positive integer ones, the corresponding lattice structure would provide more choices of filter banks, offering better trade-off between filter support and filter performance. We call such resulted filter bank as generalized-support MDLPPRFB (GSMDLPPRFB). The lattice structure for GSMDLPPRFB, however, cannot be designed by simply generalizing the process that Muramatsu employed. Furthermore, the related theories to assist the design also become different from those used by Muramatsu . Such issues will be addressed in this paper. To guide the design of GSMDLPPRFB, the necessary and sufficient conditions are established for a generalized-support multidimensional filter bank to be linear-phase. To determine the cases we can find a GSMDLPPRFB, the necessary conditions about the existence of it are proposed to be related with filter support and symmetry polarity (i.e., the number of symmetric filters ns and antisymmetric filters na). Based on a process (different from the one Muramatsu used) that combines several polyphase matrices to construct the starting block, one of the core building blocks of lattice structure, the lattice structure for GSMDLPPRFB is developed and shown to be minimal. Additionally, the result in this paper includes Muramatsu´s as a special case.
Keywords :
channel bank filters; image reconstruction; lattice filters; matrix algebra; multidimensional signal processing; Lattice structure; decimation matrix; filter performance; filter support; generalized support multidimensional linear phase perfect reconstruction filter bank; integer vectors; positive integer diagonal matrix; Equations; Filter banks; Image reconstruction; Lattices; Nickel; Symmetric matrices; Vectors; Generalized support; filter support; lattice structure; minimality; multidimensional filter bank, linear phase; perfect reconstruction; symmetry polarity;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2013.2279310
Filename :
6583951
Link To Document :
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