Section 54
Chapter 53,683

Image compression based on fuzzy algorithms for learning vector quantization and wavelet image decomposition

Karayiannis, N.B.; Pai, P.I.; Zervos, N.

IEEE Transactions on Image Processing a Publication of the IEEE Signal Processing Society 7(8): 1223-1230


ISSN/ISBN: 1057-7149
PMID: 18276335
DOI: 10.1109/83.704313
Accession: 053682908

Download citation:  

This work evaluates the performance of an image compression system based on wavelet-based subband decomposition and vector quantization. The images are decomposed using wavelet filters into a set of subbands with different resolutions corresponding to different frequency bands. The resulting subbands are vector quantized using the Linde-Buzo-Gray (LBG) algorithm and various fuzzy algorithms for learning vector quantization (FALVQ). These algorithms perform vector quantization by updating all prototypes of a competitive neural network through an unsupervised learning process. The quality of the multiresolution codebooks designed by these algorithms is measured on the reconstructed images belonging to the training set used for multiresolution codebook design and the reconstructed images from a testing set.

Full Text Article emailed within 0-6 h: $19.90