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

Author

Babb, Brendan J. ; Moore, Frank W. ; Marshall, Pat

Author_Institution

Dept. of Math. Sci., Alaska Univ., Anchorage, AK

fYear

2007

fDate

1-5 April 2007

Firstpage

202

Lastpage

207

Abstract

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

Keywords

data compression; discrete wavelet transforms; genetic algorithms; image coding; image reconstruction; image resolution; mean square error methods; discrete wavelet transforms; genetic algorithm; image compression; image reconstruction; mean squared error; multiresolution analysis transforms; quantization; scaling coefficients; wavelet coefficients; Computational intelligence; Decoding; Discrete wavelet transforms; Image coding; Image reconstruction; Multiresolution analysis; Quantization; Testing; Wavelet analysis; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence in Image and Signal Processing, 2007. CIISP 2007. IEEE Symposium on

Conference_Location

Honolulu, HI

Print_ISBN

1-4244-0707-9

Type

conf

DOI

10.1109/CIISP.2007.369318

Filename

4221419