Interpolation of Multidimensional Signals Based on Optimization of Entropy of Postinterpolation Remainders

Abstract

We analyze adaptive algorithms for interpolation of multidimensional signals and propose interpolators based on auto-switching between simple interpolation functions in each point of a signal depending on local characteristics of the signal at this point. The switching is performed with the aid of a parametrized decision rule. To fined optimal values of the parameters of this decision rule, we use a criterion of the minimum energy of postinterpolation remainders and a criterion of the minimal entropy of quantized postinterpolation remainders. We discuss results of application of the proposed interpolator for solving the problems of superimposition of heterogeneous signals and compression of signals. We used real signals and performed computer simulations, which allowed us to compare the proposed interpolators with their prototypes and to estimate the gain resulting from their implementation.