Machine Learning
Analysis and reduction of reference frames for motion estimation in MPEG-4 AVC/JVT/H.264
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Fast multiple reference frame motion estimation for H.264/AVC
IEEE Transactions on Circuits and Systems for Video Technology
Efficient Reference Frame Selector for H.264
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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In the H.264/AVC coding standard, motion estimation (ME) is allowed to use multiple reference frames to make full use of reducing temporal redundancy in a video sequence. Although it can further reduce the motion compensation errors, it introduces tremendous computational complexity as well. In this paper, we propose a statistical learning approach to reduce the computation involved in the multireference motion estimation. Some representative features are extracted in advance to build a learning model. Then, an off-line pre-classification approach is used to determine the best reference frame number according to the run-time features. It turns out that motion estimation will be performed only on the necessary reference frames based on the learning model. Experimental results show that the computation complexity is about three times faster than the conventional fast ME algorithm while the video quality degradation is negligible.