On design of linear minimum-entropy predictor

Linear predictors for lossless data compression should ideally minimize the entropy of prediction errors. But in current practice predictors of least-square type are used instead. In this paper we formulate and solve the linear minimum-entropy predictor design problem as one of convex or quasiconvex programming. The proposed minimum-entropy design algorithms are derived from the well-known fact that prediction errors of most signals obey generalized Gaussian distribution. Empirical results and analysis are presented to demonstrate the superior performance of the linear minimum-entropy predictor over the traditional least-square counterpart for lossless coding.