Development and evaluation of scalable video motion estimators on GPU

This work proposes a scalable parallelization approach for H.264/AVC motion estimation on multi-cores, and its efficient implementation on the most recent Graphical Processing Units (GPUs). Very efficient motion estimators are achieved by applying efficient data reusing techniques and exploiting the computational power of the most recent GPUs. The proposed motion estimators have been programmed on the GPU with the Tesla architecture and CUDA. Experimental results show that the proposed approach outperforms for more than 3 times motion estimators presented in the most recent publications. Moreover, real time motion estimation is achieved even for 720 times 576 resolution and 25 frames per second. The scalability of the solution is shown by implementing the motion estimators on two GPUs with the same architecture but different number of cores. Therefore, the proposed approach is useful for the more powerful future GPUs.