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Design of data dependent filter bank using sequential matrix diagonalization algorithm
Published in IEEE
Pages: 1397 - 1403
Subband coding (SBC) is a one of the popular application to filter banks to achieve multi-channel data compression. There are data independent and data dependent filter banks in the literature. In this paper, we focus on the design of data dependent M-channel maximally decimated Paraunitary (PU) filter bank using polynomial eigen value decomposition (PEVD) technique for SBC. To design this type of filter bank, we adopted a family of iterative algorithms i) Sequential matrix diagonalization (SMD) ii) Maximum element sequential matrix diagonalization (ME-SMD). The potential benefits of designed data dependent PU filter bank are strong decorrelation and spectral majorization. The SMD algorithm maximizes the coding gain at every step to minimize the subband quantization noise and to achieve the optimality of Filter bank. The performance of the proposed algorithms compared with Sequential best rotation (SBR2) and Modified sequential best rotation (SBR2C) with respect to convergence, number of iterations and coding gain. The performance of designed data dependent filter bank based on proposed algorithms are also compared with Karhunen-Loeve (KL) transform coder as it is one of data dependent optimal data compression scheme and traditional data independent PU filter banks. © 2016 IEEE.
About the journal
JournalData powered by Typeset2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)
PublisherData powered by TypesetIEEE
Open AccessNo