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Optical Engineering

Predictive vector quantization using a neural network approach
Author(s): Nader Mohsenian; Syed A. Rizvi; Nasser M. Nasrabadi
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Paper Abstract

A new predictive vector quantization (PVQ) technique capable of exploring the nonlinear dependencies in addition to the linear dependencies that exist between adjacent blocks (vectors) of pixels is introduced. The two components of the PVQ scheme, the vector predictor and the vector quantizer, are implemented by two different classes of neural networks. A multilayer perceptron is used for the predictive cornponent and Kohonen self-organizing feature maps are used to design the codebook for the vector quantizer. The multilayer perceptron uses the nonlinearity condition associated with its processing units to perform a nonlinear vector prediction. The second component of the PVQ scheme vector quantizes the residual vector that is formed by subtracting the output of the perceptron from the original input vector. The joint-optimization task of designing the two components of the PVQ scheme is also achieved. Simulation results are presented for still images with high visual quality.

Paper Details

Date Published: 1 July 1993
PDF: 11 pages
Opt. Eng. 32(7) doi: 10.1117/12.141678
Published in: Optical Engineering Volume 32, Issue 7
Show Author Affiliations
Nader Mohsenian, Princeton Univ. (United States)
Syed A. Rizvi
Nasser M. Nasrabadi, Worcester Polytechnic Institute (United States)


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