Adaptive Autoregressive Interpolation of Multidimensional Signals under Compression Based on Hierarchical Grid Interpolation

Abstract

We develop adaptive algorithms for interpolation of multidimensional signals based on a local autoregressive model of signals. Their adaptability is the result of calculation of the model’s parameters for the reading of each signal using estimates of a local autocorrelation function of the compressed signal. Under developing these interpolation algorithms, we do not include excess hierarchical grids of readings. That is the reason why our interpolators are well adapted for use in the framework of a compression method based on hierarchical grid interpolation. Computer simulations on real images confirm that the proposed interpolators allow us to increase a degree of hierarchical compression.