Neural net approach to predictive vector quantization
Nader Mohsenian, Nasser M. Nasrabadi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
A new predictive vector quantization (PVQ) technique, capable of exploring the nonlinear dependencies in addition to the linear dependencies that exist between adjacent blocks of pixels, is introduced. Two different classes of neural nets form the components of the PVQ scheme. A multi-layer perceptron is embedded in the predictive component of the compression system. This neural network, using the non-linearity condition associated with its processing units, can perform as a non-linear vector predictor. The second component of the PVQ scheme vector quantizes (VQ) the residual vector that is formed by subtracting the output of the perceptron from the original wave-pattern. Kohonen Self-Organizing Feature Map (KSOFM) was utilized as a neural network clustering algorithm to design the codebook for the VQ technique. Coding results are presented for monochrome 'still' images.