Real-time video compression using entropy-biased ANN codebooks

Stanley C. Ahalt; James E. Fowler

doi:10.1117/12.169972

2 March 1994 Real-time video compression using entropy-biased ANN codebooks

Stanley C. Ahalt, James E. Fowler

Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.169972
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

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.

Citation Download Citation

Stanley C. Ahalt and James E. Fowler "Real-time video compression using entropy-biased ANN codebooks", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.169972

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