Title :
Image compression using multilayer neural networks
Author :
Abdel-Wahhab, O. ; Fahmy, M.M.
Author_Institution :
Dept. of Electr. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
fDate :
10/1/1997 12:00:00 AM
Abstract :
A new neural-network data compression method is presented. The work extends the use of two-layer neural networks to multilayer networks. The results show the performance superiority of multilayer neural networks compared with that of the two-layer one, especially at high compression ratios. To overcome the long training time required for multilayered networks, a previously developed training algorithm has been used. A modified feedback error is proposed to reduce further the training time and to enhance the image quality. Also, a redistribution of the grey levels in the training phase is proposed to make the minimisation of the mean-square error more related to the human-vision system
Keywords :
data compression; feedback; image coding; image enhancement; least mean squares methods; multilayer perceptrons; transform coding; visual perception; autoassociative transform coding; grey levels redistribution; high compression ratios; human vision system; image compression; image quality enhancement; mean square error minimisation; modified feedback error; multilayer neural networks; neural network data compression method; performance; training algorithm; training time reduction; two-layer neural networks;
Journal_Title :
Vision, Image and Signal Processing, IEE Proceedings -
DOI :
10.1049/ip-vis:19971413