Optimal supports for linear predictive models

Linear predictive models seek to optimally extract information about a sample of a signal based on some subset of its causal past. Very little work has been done in investigating the importance and choice of this subset (support) in the prediction process. The paper addresses the problem of finding the optimal support for use by a linear predictive model. The authors derive a general result relating the distortion incurred in predicting a sample of a stationary signal based on a causal support in terms of the Wiener coefficients of a larger support and the autocorrelation matrix. Based on the above result, they derive an algorithm which optimally reduces the size of the support by one at each stage. The algorithm is tested on the Barbara image for image estimation and on the football image sequence for pel-recursive motion compensation and is shown to outperform (by large margins in some cases) conventionally chosen supports