DocumentCode :
2816264
Title :
Image compression by nonlinear principal component analysis
Author :
Yoshioka, Michifumi ; Omatu, Sigeru
Author_Institution :
Fac. of Eng., Osaka Prefecture Univ., Japan
Volume :
2
fYear :
1996
fDate :
18-21 Nov 1996
Firstpage :
704
Abstract :
In recent years, many methods for image compression have proposed, especially JPEG and MPEG have achieved high compression ratio, but these methods can not restore images completely. In these methods image data are reduced in spatial frequency domain according to human eye property. In this study, we have developed a new method to reduce image data especially in noises of image using a neural network. An advantage of this method is to preserve the quality of image by reducing the noise which is independent of original image data
Keywords :
data compression; neural nets; noise; statistical analysis; video signal processing; image compression; neural network; noise reduction; nonlinear principal component analysis; Filters; Frequency domain analysis; Humans; Image coding; Image restoration; Neural networks; Noise reduction; Principal component analysis; Source separation; Transform coding;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Technologies and Factory Automation, 1996. EFTA '96. Proceedings., 1996 IEEE Conference on
Conference_Location :
Kauai, HI
Print_ISBN :
0-7803-3685-2
Type :
conf
DOI :
10.1109/ETFA.1996.573990
Filename :
573990
Link To Document :
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