Application of adaptive constructive neural networks to image compression

Author

Ma, Liying ; Khorasani, K.

Author_Institution

Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que., Canada

Volume

13

Issue

5

fYear

2002

fDate

9/1/2002 12:00:00 AM

Firstpage

1112

Lastpage

1126

Abstract

The objective of the paper is the application of an adaptive constructive one-hidden-layer feedforward neural networks (OHL-FNNs) to image compression. Comparisons with fixed structure neural networks are performed to demonstrate and illustrate the training and the generalization capabilities of the proposed adaptive constructive networks. The influence of quantization effects as well as comparison with the baseline JPEG scheme are also investigated. It has been demonstrated through several experiments that very promising results are obtained as compared to presently available techniques in the literature.

Keywords

data compression; feedforward neural nets; generalisation (artificial intelligence); image coding; learning (artificial intelligence); OHL-FNNs; adaptive constructive neural networks; adaptive constructive one-hidden-layer feedforward neural networks; baseline JPEG scheme; constructive algorithms; feedforward neural networks; fixed structure neural networks; generalization capabilities; generalization capability; image compression; quantization effects; Adaptive systems; Cellular neural networks; Feedforward neural networks; Image coding; Image reconstruction; Image storage; Neural networks; Pixel; Quantization; Transform coding;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2002.1031943

Filename

1031943