Predictive vector quantization using neural networks

M. R. Hashemi, T. H. Yeap, S. Panchanathan

Research output: Contribution to journalConference articlepeer-review


In this paper we propose a new scalable predictive vector quantization (PVQ) technique for image and video compression. This technique has been implemented using neural networks. A Kohonen self organized feature map is used to implement the vector quantizer, while a multilayer perceptron implements the predictor. Simulation results demonstrate that the proposed technique provides a 5-10% improvement in coding performance over the existing neural networks based PVQ techniques.

Original languageEnglish (US)
Pages (from-to)14-20
Number of pages7
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Apr 1 1997
Externally publishedYes
EventApplications of Artificial Neural Networks in Image Processing II 1997 - San Jose, United States
Duration: Feb 8 1997Feb 14 1997


  • Image compression
  • Multimedia
  • Neural networks
  • Predictive vector quantization

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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