Fast H.264 Encoding Based on Statistical Learning

H.264/AVC, the latest video coding standard of the Joint Video Team, greatly outperforms previous standards in terms of coding bitrate and video quality, because it adopts several new techniques. However, the computational complexity is also considerably increased due to these new components. In this paper, we propose fast algorithms based on statistical learning to reduce the computational cost involved in three main components in H.264 encoder, i.e., intermode decision, multi-reference motion estimation (ME), and intra-mode prediction. First, representative features are extracted to build the learning models. Then, an offline pre-classification approach is used to determine the best results from the extracted features, thus a significant amount of computation is reduced based on the classification strategy. The proposed statistical learning-based approach is applied to the aforementioned three main components in H.264 encoder to speed up the computation. Experimental results show that the ME time of the proposed system is significantly sped up with 12 times faster than the conventional fast ME algorithm of H.264, and the total encoding time of the proposed encoder is greatly reduced with about four times faster than the fast encoder EPZS in the H.264 reference code with negligible video quality degradation.