Probability Based Search Motion Estimation Algorithm Using Mean Correction
Deepak Jayaswal, Mukesh A. Zaveri · International Journal of Computer and Electrical Engineering · 2010
We propose a stochastic approach to determine the motion vector (MV) for block matching algorithm (BMA).This approach allows us to exploit random distribution of motion vector in successive video frames from which the initial candidate predictors are derived.The derived predictors are the most probable points in search window, which will assure that, the motion vectors in the vicinity of center point and at the edge of the search window does not miss out, as it does for earlier algorithms like Three step search(TSS), Four step search(FSS), Diamond(DS), etc and refinement stage used in the algorithm will allow us to extract true motion vector so that the picture quality is as good as Full search(FS) which is the optimal algorithm.The novelty of the proposed algorithm is that the search pattern derived is not static but can dynamically shrink or enlarge to account for small and large motion.It is important to note that for the first time mean correction technique is introduced in video codec to improve PSNR with early termination of the algorithm.The Simulation result shows that our proposed algorithm outperforms all sub-optimal algorithms in terms of quality and speed up performance and in many cases PSNR of proposed algorithm is comparable or better than Full Search.