Training methods considering block partitioning for neural networks-based intra prediction
Dohyeon Park, Gihwa Moon, Sung-Chang Lim, Jae‐Gon Kim · 2023
This paper presents methods of Neural Network (NN) training reflecting block partitioning for Matrix-based Intra Prediction (MIP)-based networks. A training method using a dataset considering coding block partitioning leads to a NN-based predictor that is more suitable for a legacy block-based video codec compared to a training method that does not consider block partitioning. In addition, training using block partitioning of actual video encoding allows better intra prediction than a training method considering block partitioning in the training process. The MIP-based intra-prediction networks are implemented in VVC by replacing the MIP to evaluate the proposed training methods. The experimental results show that the proposed training method considering block partitioning of actual encoding gives the coding gain of 0.19% Bjøntegaard Delta (BD)-rate on average compared to training without considering block partitioning.