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Proceedings Paper

Nonlinear 1D DPCM image prediction using polynomial neural networks
Author(s): Panos Liatsis; Abir J. Hussain
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Paper Abstract

This work presents a novel polynomial neural network approach to 1D differential pulse code modulation (DPCM) design for image compression. This provides an alternative to current tradition and neural networks techniques, by allowing the incremental construction of higher-order polynomials of different orders. The proposed predictor utilizes Ridge Polynomial Neural Networks (RPNs), which allow the use of linear and non-linear terms, and avoid the problem of the combinatorial explosion of the higher-order terms. In RPNs, there is no requirement to select the number of hidden units or the order of the network. Extensive computer simulations have demonstrated that the resulting encoders work very well. At a transmission rate of 1 bit/pixel, the 1D RPN system provides on average a 13 dB improvement in SNR over the standard linear DPCM and a 9 dB improvement when compared to HONNs. A further result of the research was that third-order RPNs can provide very good predictions in a variety of images.

Paper Details

Date Published: 9 March 1999
PDF: 11 pages
Proc. SPIE 3647, Applications of Artificial Neural Networks in Image Processing IV, (9 March 1999); doi: 10.1117/12.341124
Show Author Affiliations
Panos Liatsis, Univ. of Manchester Institute of Science and Technology (United Kingdom)
Abir J. Hussain, Univ. of Manchester Institute of Science and Technology (United Kingdom)


Published in SPIE Proceedings Vol. 3647:
Applications of Artificial Neural Networks in Image Processing IV
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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