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 NNbased 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.
With the growing demand for better compression efficiency, the JVET is studying on potential future video coding technologies that can significantly exceed the compression capability of the latest standard, Versatile Video Coding (VVC). In JVET, various methods improving the existing inter prediction modes in VVC have been being discussed and the extension of Geometry Partitioning Modes (GPM) is one of them. In this paper, we propose efficient signaling methods for the extended GPM modes in the Enhanced Compression Model (ECM), which is a reference software for the exploration activity beyond VVC, based on the experimental observation of the mode occurrence frequency. The experimental results show that the proposed methods give meaningful BD-rate gain compared to ECM.
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