DocumentCode :
304467
Title :
Neural networks and semi-closed-loop predictive vector quantization for image compression
Author :
Cierniak, Robert ; Rutkowski, Leszek
Author_Institution :
Inst. of Electron. & Control Syst., Tech. Univ. of Czestochowa, Poland
Volume :
1
fYear :
1996
fDate :
16-19 Sep 1996
Firstpage :
245
Abstract :
A new algorithm for image compression, named predictive vector quantization (PVQ), is developed based on competitive neural networks and optimal linear predictors. The semi-closed-loop PVQ methodology is suggested. The experimental results are presented and the performance of the algorithm is discussed
Keywords :
differential pulse code modulation; image coding; neural nets; prediction theory; unsupervised learning; vector quantisation; DPCM; PVQ; algorithm performance; competitive neural networks; differential pulse code modulation; experimental results; image compression; optimal linear predictors; semiclosed-loop predictive vector quantization; Control systems; Decoding; Electronic mail; Image coding; Image reconstruction; Modulation coding; Neural networks; Pixel; Pulse modulation; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 1996. Proceedings., International Conference on
Conference_Location :
Lausanne
Print_ISBN :
0-7803-3259-8
Type :
conf
DOI :
10.1109/ICIP.1996.559479
Filename :
559479
Link To Document :
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