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Proceedings Paper

Real-time video compression using entropy-biased ANN codebooks
Author(s): Stanley C. Ahalt; James E. Fowler
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Paper Abstract

We describe hardware that has been built to compress video in real time using full-search vector quantization (VQ). This architecture implements a differential-vector-quantization (DVQ) algorithm which features entropy-biased codebooks designed using an artificial neural network. A special-purpose digital associative memory, the VAMPIRE chip, performs the VQ processing. We describe the DVQ algorithm, its adaptations for sampled NTSC composite- color video, and details of its hardware implementation. We conclude by presenting results drawn from real-time operation of the DVQ hardware.

Paper Details

Date Published: 2 March 1994
PDF: 12 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169972
Show Author Affiliations
Stanley C. Ahalt, The Ohio State Univ. (United States)
James E. Fowler, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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