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Fast image vector quantization using a modified competitive learning neural network approach
Author(s) -
Li Robert,
Sherrod Earnest,
Kim Jung,
Pan Gao
Publication year - 1997
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/(sici)1098-1098(1997)8:4<413::aid-ima8>3.0.co;2-d
Subject(s) - codebook , vector quantization , linde–buzo–gray algorithm , computer science , image compression , learning vector quantization , artificial neural network , quantization (signal processing) , artificial intelligence , fidelity , lookup table , image (mathematics) , image quality , algorithm , pattern recognition (psychology) , image processing , telecommunications , programming language
The basic goal of image compression through vector quantization (VQ) is to reduce the bit rate for transmission or data storage while maintaining an acceptable fidelity or image quality. The advantage of VQ image compression is its fast decompression by table lookup technique. However, the codebook supplied in advance may not handle the changing image statistics very well. The need for online codebook generation became apparent. The competitive learning neural network design has been used for vector quantization. However, its training time can be very long, and the number of output nodes is somewhat arbitrarily decided before the training starts. Our modified approach presents a fast codebook generation procedure by searching for an optimal number of output nodes evolutively. The results on two medical images show that this new approach reduces the training time considerably and still maintains good quality for recovered images. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 413–418, 1997