Optimal filter banks for signal reconstruction from noisy subband components (EN)

Delopoulos, AN (EN)

Kollias, SD (EN)

Conventional design techniques for analysis and synthesis filters in subband processing applications guarantee perfect reconstruction of the original signal from its subband components. The resulting filters, however, lose their optimality when additive noise due, for example, to signal quantization, disturbs the subband sequences. In this paper, we propose filter design techniques that minimize the reconstruction mean squared error (MSE) taking into account the second order statistics of signals and noise in the case of either stochastic or deterministic signals. A novel recursive, pseudo-adaptive algorithm is proposed for efficient design of these filters. Analysis and derivations are extended to 2-D signals and filters using powerful Kronecker product notation. A prototype application of the proposed ideas in subband coding is presented. Simulations illustrate the superior performance of the proposed filter banks versus conventional perfect reconstruction filters in the presence of additive subband noise. © 1996 IEEE. (EN)

journalArticle (EN)

Optimal Filtering (EN)

Second Order Statistics (EN)

Additive Noise (EN)

kronecker product (EN)

Subband Coding (EN)

Engineering, Electrical & Electronic (EN)

Design Technique (EN)

Filter Design (EN)

Filter Bank (EN)

Signal Reconstruction (EN)

Adaptive Algorithm (EN)

Mean Square Error (EN)

Perfect Reconstruction (EN)

WAVELET TRANSFORM (EN)

National Technical University of Athens

Digital Library of National Technical University of Athens | Dspace@NTUA

Κεντρική Βιβλιοθήκη Ε.Μ.Π.

Ιδρυματικό Αποθετήριο

Δημοσιεύσεις μελών Δ.Ε.Π. σε περιοδικά

IEEE Transactions on Signal Processing (EN)

English

1996 (EN)

http://hdl.handle.net/123456789/11985

44 (EN)

1053-587X (EN)

10.1109/78.485918 (EN)

2 (EN)

212 (EN)

224 (EN)

ISI:A1996UB15800005 (EN)

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC (EN)