In order to speed up video coding efficiency such as H.264/AVC and H265/HEVC, we propose in this paper a parallel approach of full search (FS) algorithm for motion estimation on Graphic Processor Unit (GPU). We implemented the traditional sequential FS algorithm for motion estimation to computing unified device architecture (CUDA) optimizing memory usage, taking full ad-vantage of the powerful parallel computing capability to speed up FS motion estimation. Experimental results show that our implementation on CUDA demonstrates substantial improvement up to 48 times than CPU counterpart available and can effectively speed up the FS for motion estimation
Full search, GPU, CUDA, Motion Estimation, shared memory, Optimization
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Cite this paper
Fatma Elzahra Sayadi, Marwa Chouchene, Haithem Bahri, Olfa Haggui, Bouraoui Ounir. (2017) Improved approach for full search motion estimation on GPU. International Journal of Computers, 2, 220-222
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