Adaptive predictive coding of images based upon multiplicative time series modelling

This paper formulates one-dimensional (1-D), recursive, multiplicative time series models for digital images and demonstrates their use for adaptive predictive coding of such images. The performance of the scheme presented here is superior compared to that of conventional 1-D modelling techniques, because correlation among all neighboring pixels of interest can be taken into account. Further, the projection-based constrained least squares identification technique proposed here guarantees stability of the underlying predictor, which makes the scheme more robust compared to the ones that use 2-D recursive models where predictor stability cannot be guaranteed.