Data compression using artificial neural networks.
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Author
Watkins, Bruce E.
Date
1991-09Advisor
Tummala, Murali
Second Reader
Therrien, Charles W.
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Show full item recordAbstract
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.
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