A neural network for image compression

S. Panchanathan, T. H. Yeap, B. Pilache

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations

Abstract

In this paper, we propose a new scheme for image compression using neural networks. Image data compression deals with minimization of the amount of data required to represent an image while maintaining an acceptable quality. Several image compression techniques have been developed in recent years. We note that the coding performance of these techniques may be improved by employing adaptivity. Over the last few years neural network has emerged as an effective tool for solving wide range of problems involving adaptivity and learning. , A multi-layer feed-forward neural network trained using the backward error propagation algorithm is used in many applications. However, this model is not suitable for image compression because of its poor coding performance. Recently, a self-organizing feature map (SOFM) algorithm has been proposed which yields a good coding perfonnance. However, this algorithm requires a long training time because the network starts with random initial weights. In this paper we have used the backward error propagation algorithm (BEP) to quickly obtain the initial weights which is then used to speedup the training time required by the SOFM algorithm. The proposed approach (BEP-SOFM) combines the advantages of the two techniques and hence achieves a good coding performance in a shorter training time. Our simulation results demonstrate the potential gains using the proposed technique.

Original languageEnglish (US)
Pages (from-to)376-385
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume1709
DOIs
StatePublished - Sep 16 1992
Externally publishedYes
EventApplications of Artificial Neural Networks III 1992 - Orlando, United States
Duration: Apr 20 1992 → …

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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