Optimization and assessment of wavelet packet decompositions with evolutionary computation

In image compression, the wavelet transformation is a state-of-the-art component. Recently, wavelet packet decompositionhas received quite an interest. A popular approach for wavelet packet decompositionis the near-best-basis algorithm using nonadditive cost functions. In contrast to additive cost functions, the wavelet packet decompositionof 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.