Bagged Tree and ResNet-Based Joint End-to-End Fast CTU Partition Decision Algorithm for Video Intra Coding

Abstract

Video coding standards, such as high-efficiency video coding (HEVC), versatile video coding (VVC), and AOMedia video 2 (AV2), achieve an optimal encoding performance by traversing all possible combinations of coding unit (CU) partition and selecting the combination with the minimum coding cost. It is still necessary to further reduce the encoding time of HEVC, because HEVC is one of the most widely used coding standards. In HEVC, the process of searching for the best performance is the source of most of the encoding complexity. To reduce the complexity of the coding block partition in HEVC, a new end-to-end fast algorithm is presented to aid the partition structure decisions of the coding tree unit (CTU) in intra coding. In the proposed method, the partition structure decision problem of a CTU is solved by a novel two-stage strategy. In the first stage, a bagged tree model is employed to predict the splitting of a CTU. In the second stage, the partition problem of a 32 × 32-sized CU is modeled as a 17-output classification task for the first time, so that it can be solved by a single prediction. To achieve a high prediction accuracy, a residual network (ResNet) with 34 layers is employed. Jointly using bagged tree and ResNet, the proposed fast CTU partition algorithm is able to generate the partition quad-tree structure of a CTU through an end-to-end prediction process, which abandons the traditional scheme of making multiple decisions at various depth levels. In addition, several datasets are used in this paper to lay the foundation for high prediction accuracy. Compared with the original HM16.7 encoder, the experimental results show that the proposed algorithm can reduce the encoding time by 60.29% on average, while the Bjøntegaard delta rate (BD-rate) loss is as low as 2.03%, which outperforms the results of most of the state-of-the-art approaches in the field of fast intra CU partition.