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Proceedings Paper

Fixed analysis adaptive synthesis filter banks
Author(s): Clyde A. Lettsome; Mark J. T. Smith; Russell M. Mersereau
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Paper Abstract

Subband/Wavelet filter analysis-synthesis filters are a major component in many compression algorithms. Such compression algorithms have been applied to images, voice, and video. These algorithms have achieved high performance. Typically, the configuration for such compression algorithms involves a bank of analysis filters whose coefficients have been designed in advance to enable high quality reconstruction. The analysis system is then followed by subband quantization and decoding on the synthesis side. Decoding is performed using a corresponding set of synthesis filters and the subbands are merged together. For many years, there has been interest in improving the analysis-synthesis filters in order to achieve better coding quality. Adaptive filter banks have been explored by a number of authors where by the analysis filters and synthesis filters coefficients are changed dynamically in response to the input. A degree of performance improvement has been reported but this approach does require that the analysis system dynamically maintain synchronization with the synthesis system in order to perform reconstruction. In this paper, we explore a variant of the adaptive filter bank idea. We will refer to this approach as fixed analysis adaptive synthesis filter banks. Unlike the adaptive filter banks proposed previously, there is no analysis synthesis synchronization issue involved. This implies less coder complexity and more coder flexibility. Such an approach can be compatible with existing subband wavelet encoders. The design methodology and a performance analysis are presented.

Paper Details

Date Published: 3 April 2008
PDF: 10 pages
Proc. SPIE 6979, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI, 697902 (3 April 2008); doi: 10.1117/12.784611
Show Author Affiliations
Clyde A. Lettsome, Georgia Institute of Technology (United States)
Mark J. T. Smith, Purdue Univ. (United States)
Russell M. Mersereau, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 6979:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI
Harold H. Szu; F. Jack Agee, Editor(s)

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