Adaptive L-predictors based on finite state machine context selection

In this paper we introduce a new class of adaptive nonlinear predictors by allowing the parameters of the L-predictor to be selected according to the transitions in a finite state machine (FSM) context modeller. A procedure for the adaptive design of the general unconstrained FSM-context L-predictor is proposed and compared with the classical design techniques for some particular FSM-L predictors. The application of the new predictor for lossless compression of gray level images is examined for different FSM structures.