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Compute-unified device architecture implementation of a block-matching algorithm for multiple graphical processing unit cards

Francesc Massanes, Marie Cadennes, Jovan G. Brankov
Illinois Institute of Technology, Medical Imaging Research Center, Chicago, Illinois 60616
Journal of Electronic Imaging, 20(3), 033004, 2011

@article{massanes2011compute,

   title={Compute-unified device architecture implementation of a block-matching algorithm for multiple graphical processing unit cards},

   author={Massanes, F. and Cadennes, M. and Brankov, J.G.},

   journal={Journal of Electronic Imaging},

   volume={20},

   pages={033004},

   year={2011}

}

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We describe and evaluate a fast implementation of a classical block-matching motion estimation algorithm for multiple graphical processing units (GPUs) using the compute unified device architecture computing engine. The implemented block-matching algorithm uses summed absolute difference error criterion and full grid search (FS) for finding optimal block displacement. In this evaluation, we compared the execution time of a GPU and CPU implementation for images of various sizes, using integer and noninteger search grids. The results show that use of a GPU card can shorten computation time by a factor of 200 times for integer and 1000 times for a noninteger search grid. The additional speedup for a noninteger search grid comes from the fact that GPU has built-in hardware for image interpolation. Further, when using multiple GPU cards, the presented evaluation shows the importance of the data splitting method across multiple cards, but an almost linear speedup with a number of cards is achievable. In addition, we compared the execution time of the proposed FS GPU implementation with two existing, highly optimized nonfull grid search CPU-based motion estimations methods, namely implementation of the Pyramidal Lucas Kanade Optical flow algorithm in OpenCV and simplified unsymmetrical multi-hexagon search in H.264/AVC standard. In these comparisons, FS GPU implementation still showed modest improvement even though the computational complexity of FS GPU implementation is substantially higher than non-FS CPU implementation. We also demonstrated that for an image sequence of 720 x 480 pixels in resolution commonly used in video surveillance, the proposed GPU implementation is sufficiently fast for real-time motion estimation at 30 frames-per-second using two NVIDIA C1060 Tesla GPU cards.
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