New recurrent polynomial neural network for predictive image coding

A. J. Hussain, P. Liatsis

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


This work presents a novel recurrent neural network called the recurrent Pi-sigma neural network. The proposed network has been used as 2-D predictor in differential pulse code modulation (DPCM) image coding. The advantage of this type of architecture is that it explores both the multi-linear interactions between the input pixels as well as the temporal dynamics of the image formation process. The network was trained using the dynamic backpropagation algorithm. Fifteen images were used to test the performance of the network. Extensive simulation results have shown an average peak signal to noise ratio (PSNR) of 26.2 dB at a transmission rate of 1 bit/pixel.

Original languageBritish English
Pages (from-to)82-86
Number of pages5
JournalIEE Conference Publication
Issue number465 I
StatePublished - 1999
EventProceedings of the 1999 7th International Conference on Image Processing and its Applications - Manchester, UK
Duration: 13 Jul 199915 Jul 1999


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