PREDICTIVE VECTOR QUANTIZATION USING A NEURAL-NETWORK APPROACH

A new predictive vector quantization (PVQ) technique capable of explor ing the nonlinear dependencies in addition to the linear dependencies that exist between adjacent blocks (vectors) of pixels is introduced. The two components of the PVQ scheme, the vector predictor and the vec tor quantizer, are implemented by two different classes of neural netw orks. A multilayer perceptron is used for the predictive component and Kohonen self-organizing feature maps are used to design the codebook for the vector quantizer. The multilayer perceptron uses the nonlinear ity condition associated with its processing units to perform a nonlin ear vector prediction. The second component of the PVQ scheme vector q uantizes the residual vector that is formed by subtracting the output of the perceptron from the original input vector. The joint-optimizati on task of designing the two components of the PVQ scheme is also achi eved. Simulation results are presented for still images with high visu al quality.