Predictive vector quantization using neural networks
Mahmoud Reza Hashemi, Tet Hin Yeap, Sethuraman Panchanathan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
In this paper we propose a new scalable predictive vector quantization (PVQ) technique for image and video compression. This technique has been implemented using neural networks. A Kohonen self-organized feature map is used to implement the vector quantizer, while a multilayer perceptron implements the predictor. Simulation results demonstrate that the proposed technique provides a 5 - 10% improvement in coding performance over the existing neural networks based PVQ techniques.