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Proceedings Paper

Predictive vector quantization using neural networks
Author(s): Mahmoud Reza Hashemi; Tet H. Yeap; Sethuraman Panchanathan
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Paper Abstract

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.

Paper Details

Date Published: 1 April 1997
PDF: 7 pages
Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); doi: 10.1117/12.269776
Show Author Affiliations
Mahmoud Reza Hashemi, Univ. of Ottawa (Canada)
Tet H. Yeap, Univ. of Ottawa (Canada)
Sethuraman Panchanathan, Univ. of Ottawa (Canada)


Published in SPIE Proceedings Vol. 3030:
Applications of Artificial Neural Networks in Image Processing II
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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