Compression of digital video data using artifical neural network differential vector quantization

Matthew R. Carbonara; James E. Fowler; Stanley C. Ahalt

doi:10.1117/12.140020

16 September 1992 Compression of digital video data using artifical neural network differential vector quantization

Matthew R. Carbonara, James E. Fowler, Stanley C. Ahalt

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.140020
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Matthew R. Carbonara, James E. Fowler, and Stanley C. Ahalt "Compression of digital video data using artifical neural network differential vector quantization", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140020

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