CNN-Based Driving of Block Partitioning for Intra Slices Encoding

This paper provides a technical overview of a deep-learning-based encoder method aiming at optimizing next generation hybrid video encoders for driving the block partitioning in intra slices. An encoding approach based on Convolutional Neural Networks is explored to partly substitute classical heuri...

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Bibliographic Details
Published in2019 Data Compression Conference (DCC) pp. 162 - 171
Main Authors Galpin, Franck, Racape, Fabien, Jaiswal, Sunil, Bordes, Philippe, Le Leannec, Fabrice, Francois, Edouard
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2019
Subjects
Codecs
Convolution
Convolutional neural networks
Copper
deep learning
Encoding
encoding method
Image coding
Training
video coding
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Summary:This paper provides a technical overview of a deep-learning-based encoder method aiming at optimizing next generation hybrid video encoders for driving the block partitioning in intra slices. An encoding approach based on Convolutional Neural Networks is explored to partly substitute classical heuristics-based encoder speed-ups by a systematic and automatic process. The solution allows controlling the trade-off between complexity and coding gains, in intra slices, with one single parameter. This algorithm was proposed at the Call for Proposals of the Joint Video Exploration Team (JVET) on video compression with capability beyond HEVC. In All Intra configuration, for a given allowed topology of splits, a speed-up of ×2 is obtained without BD-rate loss, or a speed-up above ×4 with a loss below 1% in BD-rate.
ISSN:2375-0359
DOI:10.1109/DCC.2019.00024