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Proceedings Paper

Compression of digital video data using artifical neural network differential vector quantization
Author(s): Matthew R. Carbonara; James E. Fowler; Stanley C. Ahalt
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Paper Abstract

A vector quantizer based on artificial neural networks is developed for use in digital video data compression applications. A series of experiments investigating the edge performance of various distortion measures and experiments exploring various vector sizes are presented. The paper then describes a differential vector quantizer which preserves edge features and an adaptive algorithm, Frequency-Sensitive Competitive Learning, which is used to develop equiprobable vector quantizer codebooks. By using codebooks comprised of equiprobable codevectors, variable length coding is unnecessary which results in robust performance in the presence of channel bit errors. The resulting coder is efficient, robust, and permits real-time hardware realizations. The DVQ coder currently under construction is also described.

Paper Details

Date Published: 16 September 1992
PDF: 12 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140020
Show Author Affiliations
Matthew R. Carbonara, Ohio State Univ. (United States)
James E. Fowler, Ohio State Univ. (United States)
Stanley C. Ahalt, Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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