Experimentation of Motion Estimation Algorithms in GPU

Abstract

Video encoder motion estimation algorithms allow a great level of parallelism exploitation, since the same arithmetic operations are repeated over near amounts of pixel data. This paper analyses the use of modern general purpose graphical processing units (GPGPU), such as the NVIDIA CUDA® as an effective acceleration engine to improve motion estimation algorithms overall performance. The results of our analysis include practical evaluations performed on different ME methods using CUDA platform. The evaluations show the impacts of the method, window search size, and ME thread mapping onto the GPGPU in the speed up that can be achieved in such parallel platform.