Title :
Compression of subband-filtered images via neural networks
Author :
Carrato, S. ; Marsi, S.
Author_Institution :
DEEI Trieste Univ., Italy
fDate :
31 Aug-2 Sep 1992
Abstract :
A novel architecture for image compression is proposed, which is based on a suitable combination of subband filtering and linear neural networks. This combination permits efficient coding, together with the advantages of the neural-network-based approach. The architecture is described, and results of simulations are presented. The architecture is shown to perform well, notwithstanding the reduced complexity of the approach. The structure is highly parallel, so that high computation rates are possible; this property can be useful if sequences of images are to be compressed
Keywords :
data compression; filtering and prediction theory; image coding; neural nets; high computation rates; image compression; image sequences; linear neural networks; parallel architecture; simulations; subband coding; subband filtering; Biomedical imaging; Electronic mail; Filtering; Image coding; Image processing; Karhunen-Loeve transforms; Medical simulation; Neural networks; Nonlinear filters; Transform coding;
Conference_Titel :
Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop
Conference_Location :
Helsingoer
Print_ISBN :
0-7803-0557-4
DOI :
10.1109/NNSP.1992.253674
