Open Access

Optimization and Assessment of Wavelet Packet Decompositions with Evolutionary Computation

EURASIP Journal on Advances in Signal Processing20032003:298617

Received: 30 June 2002

Published: 21 July 2003


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.


image compression wavelet packets best basis algorithm genetic algorithms random search

Authors’ Affiliations

Department of Scientific Computing, University of Salzburg


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