Open Access

Optimization and Assessment of Wavelet Packet Decompositions with Evolutionary Computation

EURASIP Journal on Advances in Signal Processing20032003:298617

https://doi.org/10.1155/S111086570330407X

Received: 30 June 2002

Published: 21 July 2003

Abstract

In image compression, the wavelet transformation is a state-of-the-art component. Recently, wavelet packet decomposition has received quite an interest. A popular approach for wavelet packet decomposition is the near-best-basis algorithm using nonadditive cost functions. In contrast to additive cost functions, the wavelet packet decomposition of the near-best-basis algorithm is only suboptimal. We apply methods from the field of evolutionary computation (EC) to test the quality of the near-best-basis results. We observe a phenomenon: the results of the near-best-basis algorithm are inferior in terms of cost-function optimization but are superior in terms of rate/distortion performance compared to EC methods.

Keywords

image compressionwavelet packetsbest basis algorithmgenetic algorithmsrandom search

Authors’ Affiliations

(1)
Department of Scientific Computing, University of Salzburg

Copyright

© Copyright © 2003 Hindawi Publishing Corporation 2003