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As a crucial part of video compression, intra prediction utilizes localinformation of images to eliminate the redundancy in spatial domain. In boththe High Efficiency Video Coding (H.265/HEVC) and Versatile Video Coding(H.266/VVC), multiple directional prediction modes are employed to find thetexture trend of each small block and then the prediction is made based onreference samples in the selected direction. Recently, the intra predictionschemes based on neural networks have achieved great success. In these methods,the networks are trained and applied to intra prediction to assist thedirectional prediction modes. In this paper, we propose a novel tree-structureddata clustering-driven neural network (dubbed TreeNet) for intra prediction,which builds the networks and clusters the training data in a tree-structuredmanner. Specifically, in each network split and training process of TreeNet,every parent network on a leaf node is split into two child networks by addingor subtracting Gaussian random noise. Then a data clustering-driven training isapplied to train the two derived child networks using the clustered trainingdata of their parent. To test the performance, TreeNet is integrated into VVCand HEVC to combine with or replace the directional prediction modes. Inaddition, a fast termination strategy is proposed to accelerate the search ofTreeNet. The experimental results demonstrate that TreeNet with the fasttermination can reach an average of 2.8% Bjontegaard distortion rate (BD-rate)improvement (up to 8.1%) and 4.9% BD-rate improvement (up to 8.2%) over VVC(VTM-4.0) and HEVC (HM-16.9) with all intra configuration, respectively.

Tree-Structured Data Clustering-Driven Neural Network for Intra Prediction in Video Coding