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March 7, 2009

Evolved Transforms and Image Compression

Image compression is a key task performed by image processing software. The most widely used compression schemes, such as wavelet transforms, usually introduce a loss of information while reducing the number of bits required for image representation. A new approach which uses evolutionary computation (EC) circumvents the problem of data loss.

A team of computer scientists from the University of Alaska Anchorage and Wright-Patterson Air Force Base in Ohio are using EC's fitness-based selection and genetics-inspired operators to evolve solutions for image compression optimization. Working since 2004, Frank Moore, Brendan Babb, Michael Peterson, and Gary Lamont have found that EC can outperform wavelets for compression tasks. Ongoing research is underway on the optimization of satellite images. Future applications could include radio frequency signal compression and noise reduction in medical imaging.

Posted by BlogAuthor1 at March 7, 2009 3:41 PM
category: Evolutionary Computation


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