The research described in this paper uses a genetic algorithm (GA) to evolve wavelet and scaling coefficients for transforms that outperform discrete wavelet transforms (DWTs) under conditions subject to quantization. Compression and reconstruction transform pairs evolved against a representative training image reduce mean squared error (MSE) by more than 22% (1.126 dB) when subsequently applied to test images at a single level of decomposition, while evolved three-level multiresolution analysis (MRA) transforms average more than 11% (0.50 dB) MSE reduction when applied to test images in comparison to the Daubechies-4 (D4) wavelet, without increasing the size of the compressed file.


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    Title :

    Evolved Multiresolution Analysis Transforms for Improved Image Compression and Reconstiruction under Quantization


    Contributors:


    Publication date :

    2007-04-01


    Size :

    6823386 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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