Abstract: Due to the good rate-distortion performance,as the latest video compression standard,high efficiency video coding (HEVC) has been adopted by more and more terminals.However,there are still a large number of H.264 streams in the field of video compression.Therefore,H.264 to HEVC video transcoding is a meaningful research issue.The simplest way to achieve H.264 to HEVC transcoding is to directly cascade the H.264 decoder and the HEVC encoder.Due to high complexity of the HEVC coding process,this transcoding method is time-consuming.Therefore,this paper proposed a fast H.264 to HEVC transcoding method based on deep learning to predict the CTU(Coding Tree Unit) partition of HEVC,avoiding the brute-force search of CTU partition for rate-distortion optimization(RDO).First,a large-scale database of H.264 to HEVC transcoding is built for ensuring the training of deep learning model.Second,the correlation between HEVC CTU partition and H.264 domain features is analyzed,and the similarity of CTU partition across frames is found out.Then,a three-level classifier based on LSTM (Long Short-Term Memory) is designed to predict the CTU partition.The experimental results show that the H.264 to HEVC fast transcoding algorithm proposed in this paper achieves 60% reduction in complexity compared to the original transcoder,while the peak signal-to-noise ratio is only reduced by 0.039kdB,so the proposed method outperforms the state-of-the-art transcoding methods.

Key words: Deep learning, H.264, HEVC, Video transcoding