Entropy-constrained linear vector prediction for motion-compensated video coding

Theoretical and experimental results for motion-compensated linear prediction of video signals are presented. The rate distortion theory of motion-compensated prediction is extended to linear predictive models. For practical implementations, linear vector predictors are employed. The rate of the motion data is controlled by minimizing a Lagrangian cost function where rate is weighted against distortion using a Lagrange multiplier. An adaptive algorithm for optimally selecting the number of input signals to the linear vector predictor is given