DocumentCode :
1032890
Title :
Improved colour image vector quantisation by means of self-organising neural networks
Author :
Galli, I. ; Mecocci, A. ; Cappellini, Valeria
Author_Institution :
Dept. of Electron. Eng., Florence Univ.
Volume :
30
Issue :
4
fYear :
1994
fDate :
2/17/1994 12:00:00 AM
Firstpage :
333
Lastpage :
334
Abstract :
The problem of colour quantisation is important in many respects: colour monitors can usually display only a number of contemporary colours, and some images need to be represented in an approximate, even if satisfying, way. This is particularly true for the dissemination of images through communications networks and to information terminals. Moreover, colour quantised images can be stored in less space. The colour quantisation algorithm introduced by the authors is based on a set of neural cells structured in a self-organising two-dimensional map. The proposed technique provides high quality images and its neural architecture makes the algorithm flexible even if different kinds of source image are used. The hardware implementation is easy to realise and gives real-time performance
Keywords :
colour; image coding; self-organising feature maps; vector quantisation; SNR; colour image vector quantisation; colour monitors; communications networks; image dissemination; neural architecture; real-time performance; self-organising neural networks; self-organising two-dimensional map;
fLanguage :
English
Journal_Title :
Electronics Letters
Publisher :
iet
ISSN :
0013-5194
Type :
jour
DOI :
10.1049/el:19940210
Filename :
267315
Link To Document :
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