Evolved image compression transforms

State-of-the-art image compression and reconstruction schemes utilize wavelets. Quantization and thresholding are commonly used to achieve additional compression, but cause permanent, irreversible information loss. This paper describes an investigation into whether evolutionary computation (EC) may be used to optimize forward (compression-only) transforms capable of matching or exceeding the compression capabilities of a selected wavelet, while reducing the aggregate error in images subsequently reconstructed by that wavelet. Transforms are independently trained and tested using three sets of images: digital photographs, fingerprints, and satellite images.

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