  As a crucial part of video compression, intra prediction utilizes local
information of images to eliminate the redundancy in spatial domain. In both
the High Efficiency Video Coding (H.265/HEVC) and Versatile Video Coding
(H.266/VVC), multiple directional prediction modes are employed to find the
texture trend of each small block and then the prediction is made based on
reference samples in the selected direction. Recently, the intra prediction
schemes based on neural networks have achieved great success. In these methods,
the networks are trained and applied to intra prediction to assist the
directional prediction modes. In this paper, we propose a novel tree-structured
data clustering-driven neural network (dubbed TreeNet) for intra prediction,
which builds the networks and clusters the training data in a tree-structured
manner. Specifically, in each network split and training process of TreeNet,
every parent network on a leaf node is split into two child networks by adding
or subtracting Gaussian random noise. Then a data clustering-driven training is
applied to train the two derived child networks using the clustered training
data of their parent. To test the performance, TreeNet is integrated into VVC
and HEVC to combine with or replace the directional prediction modes. In
addition, a fast termination strategy is proposed to accelerate the search of
TreeNet. The experimental results demonstrate that TreeNet with the fast
termination 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.
