A Low-Complexity Fast CU Partitioning Decision Method Based on Texture Features and Decision Trees

Abstract

The rapid advancement of information technology, particularly in artificial intelligence and communication, is driving significant transformations in video coding. There is a steadily increasing demand for high-definition video in society. The latest video coding standard, versatile video coding (VVC), offers significant improvements in coding efficiency compared with its predecessor, high-efficiency video coding (HEVC). The improvement in coding efficiency is achieved through the introduction of a quadtree with nested multi-type tree (QTMT). However, this increase in coding efficiency also leads to a rise in coding complexity. In an effort to decrease the computational complexity of VVC coding, our proposed algorithm utilizes a decision tree (DT)-based approach for coding unit (CU) partitioning. The algorithm uses texture features and decision trees to efficiently determine CU partitioning. The algorithm can be summarized as follows: firstly, a statistical analysis of the new features of the VVC is carried out. More representative features are considered to extract to train classifiers that match the framework. Secondly, we have developed a novel framework for rapid CU decision making that is specifically designed to accommodate the distinctive characteristics of QTMT partitioning. The framework predicts in advance whether the CU needs to be partitioned and whether QT partitioning is required. The framework improves the efficiency of the decision-making process by transforming the partition decision of QTMT into multiple binary classification problems. Based on the experimental results, it can be concluded that our method significantly reduces the coding time by 55.19%, whereas BDBR increases it by only 1.64%. These findings demonstrate that our method is able to maintain efficient coding performance while significantly saving coding time.