Video transcoding is to convert one compressed video stream to another. In this paper, a fast H.264/AVC to High Efficiency Video Coding (HEVC) transcoding method based on machine learning is proposed by considering the similarity between compressed streams, especially the block partition correlations, to reduce the computational complexity. This becomes possible by constructing three-level binary classifiers to predict quad-tree Coding Unit (CU) partition in HEVC. Then, we propose a feature selection algorithm to get representative features to improve predication accuracy of the classification. In addition, we propose an adaptive probability threshold determination scheme to achieve a good trade-off between low coding complexity and high compression efficiency during the CU depth prediction in HEVC. Extensive experimental results demonstrate the proposed transcoder achieves complexity reduction of 50.2% and 49.2% on average under lowdelay P main and random access configurations while the rate-distortion degradation is negligible. The proposed scheme is proved more effective as comparing with the state-of-the-art benchmarks.
|Journal||Journal of Visual Communication and Image Representation|
|Publication status||Published - 1 Jul 2016|
Bibliographical noteThis work was supported by the National Natural Science Foundation of China under Grants 61471348, U1301257 and 61272289, in part by Shenzhen Overseas High-Caliber Personnel Innovation and Entrepreneurship Project under Grant KQCX20140520154115027, and in part by Guangdong Special Support Program for Youth Science and Technology Innovation Talents under Grant 2014TQ01X345, in part by the National High-tech R&D Program of China under Grant 2015AA015901, and by Zhejiang Provincial Natural Science Foundation of China under Grant LY15F010005 .
- Block partition similarity
- Feature selection
- High Efficiency Video Coding
- Machine learning