Data compression using artificial neural networks.
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Authors
Watkins, Bruce E.
Subjects
Neural Networks
Vector Quantization
Image Coding
Vector Quantization
Image Coding
Advisors
Tummala, Murali
Date of Issue
1991-09
Date
September 1991
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
This thesis investigates the application of artificial neural networks for the compression
of image data. An algorithm is developed using the competitive learning
paradigm which takes advantage of the parallel processing and classification capability
of neural networks to produce an efficient implementation of vector quantization.
Multi-Stage, tree searched, and classification vector quantization codebook design
techniques are adapted to the neural network design to reduce the computational
cost and hardware requirements. The results show that the new algorithm provides
a substantial reduction in computational costs and an improvement in performance.
Type
Thesis
Description
Series/Report No
Department
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
84 p.;28 cm.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.