Parallelization of the hierarchical search in Python for high performance embedded systems

The number of high performance embedded systems that are used for multimedia applications, like video encoding or decoding, has erupted. A key component in video encoding is the motion estimation, which exhibits high computational complexity and hard to meet deadlines. The most popular technique for motion estimation is block matching. The hierarchical search (HS) is a popular and a very fast block matching algorithm that achieves the best image quality, with a very high computational complexity. This complexity is usually handled using parallelization. Our work differentiates from other authors, because it targets parallelization on embedded systems using the Python framework and specifically the Multiprocessing module. The experimental results on parallelization of the HS algorithm on a high performance multi core embedded systems, illustrate the usefulness of our methodology, with speedup up to 1.4.