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Effective Image Compression using Evolved Wavelets

Uli Grasemann
Department of Computer Sciences
The University of Texas at Austin
Austin, TX 78712

Risto Miikkulainen
Department of Computer Sciences
The University of Texas at Austin
Austin, TX 78712

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ABSTRACT
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 ngerprints. In
this paper, a method based on the coevolutionary genetic
algorithm introduced in [11] is used to evolve specialized
wavelets for ngerprint images. These wavelets are compared
to the hand-designed wavelet currently used by the
FBI to compress ngerprints. The results show that the
evolved wavelets consistently outperform the hand-designed
wavelet. Using evolution to adapt wavelets to classes of images
can therefore signicantly increase the quality of compressed
images.


INTRODUCTION
Image compression is one of the most important and successful
applications of the wavelet transform. Mature waveletbased
image coders like the JPEG2000 standard [15] are
available, gaining in popularity, and easily outperform traditional
coders based on the discrete cosine transform (DCT)
like JPEG [25].
Unlike in DCT-based image compression, however, the
performance of a wavelet-based image coder depends to a
large degree on the choice of the wavelet. This problem
is usually handled by using standard wavelets that are not
specially adapted to a given image, but that are known to
perform well on photographic images.
However, many common classes of images do not have the
same statistical properties as photographic images, such as
ngerprints, medical images, scanned documents, and satellite
images. The standard wavelets used in image coders
often do not match such images, resulting in decreased compression
or image quality. Moreover, non-photographic images
are often stored in large databases of similar images,
making it worthwile to nd a specially adapted wavelet for
them. As Chris Brislawn, one of the architects of WSQ [13],
the FBI's standard for ngerprint compression, states