  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 $\times 2$ is obtained without
BD-rate loss, or a speed-up above $\times 4$ with a loss below 1\% in BD-rate.
