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%.

At present, the latest video coding standard is Versatile Video Coding (VVC). Although the coding efficiency of VVC is significantly improved compared to the previous generation, standard High-Efficiency Video Coding (HEVC), it also leads to a sharp increase in coding complexity. VVC significantly improves HEVC by adopting the quadtree with nested multi-type tree (QTMT) partition structure, which has been proven to be very effective. This paper proposes a low-complexity fast coding unit (CU) partition decision method based on the light gradient boosting machine (LGBM) classifier. Representative features were extracted to train a classifier matching the framework. Secondly, a new fast CU decision framework was designed for the new features of VVC, which could predict in advance whether the CU was divided, whether it was divided by quadtree (QT), and whether it was divided horizontally or vertically. To solve the multi-classification problem, the technique of creating multiple binary classification problems was used. Subsequently, a multi-threshold decision-making scheme consisting of four threshold points was proposed, which achieved a good balance between time savings and coding efficiency. According to the experimental results, our method achieved a significant reduction in encoding time, ranging from 47.93% to 54.27%, but only improved the Bjøntegaard delta bit-rate (BDBR) by 1.07%~1.57%. Our method showed good performance in terms of both encoding time reduction and efficiency.

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.