Some new results in nonlinear predictive image coding using neural networks

The problem of nonlinear predictive image coding with multilayer perceptrons is considered. Some important aspects of coding, including the training of multilayer perceptrons, the adaptive scheme, and the robustness to the channel noise, are discussed in detail. Computer simulation results show that nonlinear predictors have better predictive performances than the linear DPCM. It is shown that the nonlinear predictor will produce smaller variance of predictive error than the linear predictor; that in the absence of the channel noise the nonlinear predictor can provide about a 3-dB improvement in signal-to-noise ratio over the linear one at the same transmission bit rate; and that, after being specially trained, the nonlinear predictor has a stronger robustness to the channel noise than the linear one