Fast intermode decision via statistical learning for H.264 video coding

Authors:
Wei-Hau Pan;Chen-Kuo Chiang;Shang-Hong Lai
Affiliations:
Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan;Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan;Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
Venue:
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Year:
2008

Citing 3
Cited 1

Fundamentals of digital image processing

Fundamentals of digital image processing
Fast intermode decision in H.264/AVC video coding

IEEE Transactions on Circuits and Systems for Video Technology
Fast Variable Block Size Motion Estimation for H.264 Using Likelihood and Correlation of Motion Field

IEEE Transactions on Circuits and Systems for Video Technology

Fast H.264 encoding based on statistical learning

PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II

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Abstract

Although the variable-block-size motion compensation scheme significantly reduces the compensation error, the computational complexity of motion estimation (ME) is tremendously increased at the same time. To reduce the complexity of the variable-block-size ME algorithm, we propose a statistical learning approach to simplify the computation involved in the sub-MB mode selection. Some representative features are extracted during ME with fixed sizes. Then, an off-line pre-classification approach is used to predict the most probable sub-MB modes according to the run-time features. It turns out that only possible sub-MB modes need to perform ME. Experimental results show that the computation complexity is significantly reduced while the video quality degradation and bitrate increment is negligible.