Nonlinear 1-D DPCM image prediction using polynomial neural networks

Panos Liatsis, Abir J. Hussain

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations


This work presents a novel polynomial neural network approach to 1-D differential pulse code modulation (DPCM) design for image compression. This provides an alternative to current traditional 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 (as in multi-layer perceptrons) or the order of the network (as in higher-order neural networks). Extensive computer simulations have demonstrated that the resulting encoders work very well. At a transmission rate of 1 bit/pixel, the 1-D 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.

Original languageBritish English
Pages (from-to)58-68
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 1999
EventProceedings of the 1999 Applications of Artificial Neural Networks in Image Processing IV - San Jose, CA, USA
Duration: 28 Jan 199929 Jan 1999


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