Wavelet-based image coders like the JPEG2000 standard are the state of the art in image compression.
Unlike traditional image coders, however, their performance depends to a large degree on the choice of
a good wavelet.
Most wavelet-based image coders use standard wavelets that are known to perform well on photographic images.
However, these wavelets do not perform as well on other common image classes, like scanned documents or fingerprints.
In this paper, a method based on the coevolutionary genetic algorithm introduced in  is used to evolve
specialized wavelets for fingerprint images.
These wavelets are compared to the hand-designed wavelet currently used by the FBI to compress fingerprints.
The results show that the evolved wavelets consistently outperform the hand-designed wavelet.
Using evolution to adapt wavelets to classes of images can therefore significantly increase the quality of compressed images.
[ Bronze Medal in the GECCO-2005 Human-Competitive Results Competition ]