Fast motion estimation within the H.264 codec
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Intra frame encoding using programmable graphics hardware
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
Motion- and aliasing-compensated prediction for hybrid video coding
IEEE Transactions on Circuits and Systems for Video Technology
Variable block-size transforms for H.264/AVC
IEEE Transactions on Circuits and Systems for Video Technology
Overview of the Scalable Video Coding Extension of the H.264/AVC Standard
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
QoS and resource management in distributed interactive multimedia environments
Multimedia Tools and Applications
Exploration of motion estimation algorithm in graphics processing environment
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Evaluation of CUDA GPU architecture as H.264 intra coding acceleration engine
Proceedings of the 19th Brazilian symposium on Multimedia and the web
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The scalable video coding (SVC), an extension of H.264/MPEG4-AVC (H.264), was standardized in 2007 by Joint Video Team (JVT). SVC provides spatial, temporal and SNR scalabilities. To achieve these scalabilities, SVC uses additional coding tools and coding modes based on H.264. The coding tools used by SVC and the variety coding modes decision make the corresponding coding complexity become extremely high, so real-time realization of SVC is nearly impossible by using software and single-core CPU only. One possible solution to generate SVC streams in real-time is to parallelize the whole encoding process. Currently, multi-core CPU and GPU are two popular kinds of parallel processing architectures. Not much research has been devoted to realize the parallel SVC encoders based on the co-work of these two architectures. In this paper, a scalable computation model for spatial SVC using multi-core CPU and GPGPU through NVIDIA CUDA is proposed. On the basis of the proposed computational model, a solution to solve the challenging data transition problem (will be detailed later) of this CPU-GPU co-work architecture is then provided. Simulation results show that, through our work, significant speed up gain in spatial SVC encoding can be achieved.