Scalable Parallel Programming with CUDA
Queue - GPU Computing
Low-cost, high-speed computer vision using NVIDIA's CUDA architecture
AIPR '08 Proceedings of the 2008 37th IEEE Applied Imagery Pattern Recognition Workshop
Accelerating leukocyte tracking using CUDA: A case study in leveraging manycore coprocessors
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Fast motion estimation on graphics hardware for H.264 video encoding
IEEE Transactions on Multimedia
Highly parallel rate-distortion optimized intra-mode decision on multicore graphics processors
IEEE Transactions on Circuits and Systems for Video Technology
An efficient three-step search algorithm for block motion estimation
IEEE Transactions on Multimedia
Novel Point-Oriented Inner Searches for Fast Block Motion Estimation
IEEE Transactions on Multimedia
Error Concealment for Frame Losses in MDC
IEEE Transactions on Multimedia
Hardware architecture design of video compression for multimedia communication systems
IEEE Communications Magazine
Standard compatible extension of H.263 for robust video transmission in mobile environments
IEEE Transactions on Circuits and Systems for Video Technology
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
Accelerate video decoding with generic GPU
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Fast Motion Estimation With Interpolation-Free Sub-Sample Accuracy
IEEE Transactions on Circuits and Systems for Video Technology
Subsampled Block-Matching for Zoom Motion Compensated Prediction
IEEE Transactions on Circuits and Systems for Video Technology
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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.