Machine learning for arbitrary downsizing of pre-encoded video in HEVC

In this paper, we propose a machine learning based transcoding scheme for arbitrarily downsizing a pre-encoded High Efficiency Video Coding video. The spatial scaling factor can be freely selected to adapt the output bit rate to the bandwidth of the network. Furthermore, machine learning techniques can exploit the correlation between input and output coding information to predict the split-flag of coding units in a P-frame.

We analyzed the performance of both offline and online training in the learning phase of transcoding. The experimental results show that the proposed techniques significantly reduce the transcoding complexity and achieve trade-offs between coding performance and complexity. In addition, we demonstrate that online training performs better than offline training.

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