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Optimization and Assessment of Wavelet Packet Decompositions with Evolutionary Computation

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.

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Correspondence to Thomas Schell.

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Schell, T., Uhl, A. Optimization and Assessment of Wavelet Packet Decompositions with Evolutionary Computation. EURASIP J. Adv. Signal Process. 2003, 298617 (2003). https://doi.org/10.1155/S111086570330407X

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Keywords

  • image compression
  • wavelet packets
  • best basis algorithm
  • genetic algorithms
  • random search