Compression of digital video data using artifical neural network differential vector quantization
Matthew R. Carbonara, James E. Fowler, Stanley C. Ahalt · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
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.