Combining stochastic filters with fast search motion estimation algorithms: Performance analysis

Fast search motion estimation algorithms when compared to full search motion estimation algorithms often converge to a local minimum, providing a momentous reduction in computational cost. However, the motion vectors measurement process in fast search algorithms is subject to noise and matching errors. Researchers have investigated the use of mathematical tools used for stochastic estimation from noisy measurements, to seek optimal estimates. Amongst those tools, is the conventional Kalman filtering, which addresses the general problem of trying to estimate the state of a discrete-time controlled process that is governed by the linear stochastic difference. This research investigates the possible combinations of benchmark motion estimation algorithms and the Kalman filter. In this paper, the author presents an in-depth investigation and a detailed analysis on the use of the above combination, and seeks to establish conditions under which the application would be successful. Experimental results show that the above is possible only under certain conditions and constraints of certain properties of the video sequences being coded. Furthermore, a recommendation is made on when it is possible to use the adaptive Kalman filter instead of the conventional filter to enhance the motion vectors at the cost of extra complexity.