Convolutional Neural Network-based Split Prediction for VVC Intra Speedup

Versatile Video Coding (VVC) achieves promising compression performances compared with High Efficiency Video Coding (HEVC) while sacrificing the encoding speed. This paper focuses on predicting the partitioning structures with convolutional neural networks to speedup the VVC encoder. Specifically, we formulate the partitioning prediction problem into two alternatives: implicit partitioning prediction based on the split type of subblock boundaries and explicit partitioning prediction from the ensemble partitioning space. Then, we address both formulations using convolutional neural networks.