A generalized model for Kalman filtering of motion information in video compression

In this paper, we tackle the problem of motion estimation in video compression. Since Full Search Algorithms (FSA) present the disadvantage of adding a high computational burden to the encoder, fast search techniques have been used in conjunction with predictive filtering, in such a way to guarantee an acceptable quality with an affordable complexity. The aim of this work is to propose a novel framework for Kalman filtering of motion information in compressed video sequences. The merits of our new framework are twofold: First, using an appropriate formulation of the system equations, several shortcomings inherent with former models in the literature are greatly counteracted. Secondly, it is constructed using a generalized structure in such a way to enclose a large variety of prediction models. Therefore, it can adapt to different types of motion activities in video sequences, without the need for a different formulation in each prediction model, as was the case in previous studies. Furthermore, we propose an adaptive motion compensation technique that permits an additional improvement to the decoded video quality. Our framework permits a considerable gain in the average performance compared to previous models and even to the FSA technique.