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Effective Image Compression using Evolved Wavelets - username - 08-17-2017 [attachment=5801] 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 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 |