A parallel algorithm for motion estimation in video coding using the bilinear transformation

Accurate motion estimation between frames is important for drastically reducing data redundancy in video coding. However, advanced motion estimation methods are computationally intensive and their execution in real time usually requires a parallel implementation. In this paper, we investigate the parallel implementation of such a motion estimation technique. Specifically, we present a parallel algorithm for motion estimation based on the bilinear transformation on the well-known parallel model of the hypercube network and formally prove the time and the space complexity of the proposed algorithm. We also show that the parallel algorithm can also run on other hypercubic networks, such as butterfly, cube-connected-cycles, shuffle-exchange or de Bruijn network with only constant slowdown.