Fast CU Partitioning Algorithm for VVC Based on Multi-Stage Framework and Binary Subnets

Abstract

VVC is the latest video compression technology available, and while the coding efficiency has improved significantly over the previous generation of standards, it has also led to a dramatic increase in coding complexity. As VVC uses a QTMT division structure, the more flexible division structure also allows for a significant increase in coding time. We have built a multi-stage network framework to solve the above problem by dividing the CU into different stages according to the size of the blocks. The desired features are extracted by dynamically adjusting to the size of the input CU. Secondly, we construct a binary classification subnet to perform the classification task at each stage and can determine the QT and MT division decisions. Finally, the resulting experimental results can demonstrate that our novel two-threshold decision scheme can achieve a balance between RD performance and TS. Our method succeeds in reducing the coding time by 49.08% to 52.56%, while the complexity of the negligible BD-BR increases by only 1.10% to 1.42%.INDEX TERMS Versatile video coding, intra coding, fast coding algorithm, CNN, deep learning.

Extreme Learning Machine-Enabled Coding Unit Partitioning Algorithm for Versatile Video Coding

“…Li et al introduce a swift CU partitioning decision method that combines texture complexity and CNNs [14], and this method begins by analyzing CU segmentation patterns from the training set, processing large CU blocks based on texture complexity. Wang et al develop a multi-stage network framework for addressing the given issue, segmenting CUs into stages based on block size and adaptively extracting relevant features [15]. Tissier et al propose a decision tree (DT) model to predict probable splits for each block [16].…”