# CNN-based driving of block partitioning for intra slices encoding

arXiv: https://arxiv.org/abs/2011.06691 · HTML: https://mlsanity.com/paper/2011.06691/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation](https://mlsanity.com/paper/2205.03587/) (2022)
- [Partition Map-Based Fast Block Partitioning for VVC Inter Coding](https://mlsanity.com/paper/2504.18398/) (2025)
- [CNN-based Prediction of Partition Path for VVC Fast Inter Partitioning Using Motion Fields](https://mlsanity.com/paper/2310.13838/) (2023)

## Abstract (arXiv)

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.