Adaptive L-Predictors Based on Finite State Machine Context Selection
نویسندگان
چکیده
In this paper we introduce a new class of adap-tive nonlinear predictors by allowing the parameters of the L-predictor to be selected according to the transitions in a Finite State Machine context modeller. A procedure for the adaptive design of the general un-constrained 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 diierent FSM structures.
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