Probability Based Adaptive Search Motion Estimation Algorithm

We propose a probabilistic 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 search (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). The novelty of the proposed algorithm fast probability based adaptive search motion estimation algorithm (PASME) is that the search pattern derived is not static but can dynamically shrink or enlarge to account for small and large motion and fixed threshold with mean correction is used to reduce computational complexity without compromising for quality of picture in terms of PSNR. The Simulation result shows that our proposed algorithm performs better than the sub-optimal algorithms in terms of quality and speed up performance and in many cases PSNR of proposed algorithm is comparable and better to Full Search.