Recurrent pi-sigma networks for DPCM image coding

A. J. Hussain, Panos Liatsis

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

This work proposes a new recurrent polynomial neural network that utilises both the temporal dynamics of the image formation process and the multi-linear interactions between the pixels for 1D/2D predictive image coding. The network consists of a layer of summing units followed by a product unit and incorporates a feedback link from the output to the input layer. It is trained using a small size training set through dynamic backpropagation. Its performance is evaluated on a database of 15 images and compared to the higher-order neural network, the feed-forward pi-sigma neural network, and the standard linear predictor.

Original languageBritish English
Pages (from-to)363-382
Number of pages20
JournalNeurocomputing
Volume55
Issue number1-2
DOIs
StatePublished - Sep 2003

Keywords

  • Differential pulse code modulation
  • Higher-order neural networks
  • Image compression
  • Recurrent networks

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