Vector quantization and signal compression
Vector quantization and signal compression
Ten lectures on wavelets
Genetic Algorithm Wavelet Design for Signal Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera models and machine perception
Camera models and machine perception
Effective image compression using evolved wavelets
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Mathematical Theory of Communication
A Mathematical Theory of Communication
A satellite image set for the evolution of image transforms for defense applications
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Image sets for the training of image processing systems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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Government, commercial, scientific, and defense applications inimage processing often require transmission of large amounts of data across bandwidth-limited channels. Applications require robust transforms simultaneously minimizing bandwidth requirements and image resolution loss. Image processing algorithms take advantage of quantization to provide substantial lossy compression ratios at the expense of resolution. Recent research demonstrates that genetic algorithms evolve filters outperforming standard discrete wavelet transforms in conditions subject to high quantization error. While evolved filters improve overall image quality, wavelet filters typically provide a superior high frequency response, demonstrating improved reconstruction near the edges of objects within an image. This paper presents an algorithm to generate transform filters that optimize edge reconstruction, improving object edge resolution by up to 24%. Such filters provide an increased object resolution over standard wavelets and traditionally evolved filters for varied applications of image processing.