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Motion vector extrapolation for parallel motion estimation on GPU

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Abstract

The powerful parallel computing ability of Graphics Processing Unit (GPU) has shown its striking superiority for motion estimation acceleration in conventional hybrid video encoding process. Unfortunately, the motion information of the neighboring macroblocks is not available for current macroblock, such that parallel motion estimation using GPU is not very favored. To tackle this problem while achieving high acceleration ration, motion vector cost is always ignored in most existing solutions, which inevitably causes severe rate-distortion loss. In this paper, a novel motion vector extrapolation based approach (MVEA) is presented for enhancing rate-distortion performance of parallel motion estimation on GPU, which is based on the study of motion vector recovery strategies for frame loss error concealment. Furthermore, the efficient implementation of MVEA on Computing Unified Device Architecture (CUDA) is also investigated. Simulation results show that MVEA can achieve a maximum peak Signal-to-Noise ratio enhancement of 0.8 dB with ignorable computational cost increase.

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Acknowledgments

The authors are thankful for the writing help from Prof. Lai-Man Po at City University of Hong Kong. We also thank the anonymous reviewers for their time and valuable comments which helped improve the paper quality significantly.

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  1. Wuhan Digital Engineering Institute, Wuhan, 430074, People’s Republic of China

    Yi Gao & Jun Zhou

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  1. Yi Gao
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  2. Jun Zhou
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Correspondence to Yi Gao.

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Gao, Y., Zhou, J. Motion vector extrapolation for parallel motion estimation on GPU. Multimed Tools Appl 68, 701–715 (2014). https://doi.org/10.1007/s11042-012-1074-4

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