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...

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Bibliographic Details
Published in2015 IEEE International Conference on Consumer Electronics (ICCE) pp. 406 - 407
Main Authors Luong Pham Van, De Praeter, Johan, Van Wallendael, Glenn, De Cock, Jan, Van de Walle, Rik
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2015
Subjects
Bit rate
Complexity theory
Predictive models
Training
Transcoding
Video coding
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Summary: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.
ISSN:2158-3994
2158-4001
DOI:10.1109/ICCE.2015.7066464