The latest generation of coding standard, Versatile Video Coding (VVC), has achieved more bitrate reduction compared with high efficiency video coding. However, the introduction of quadtree with nested Multi-Type Tree (MTT) coding structure greatly increases the computational complexity. To reduce the complexity of VVC, a Support Vector Machine (SVM) based Coding Unit (CU) size decision algorithm is presented. Firstly, effective features, derived from entropy, texture contrast, and Haar wavelet efficient of current CU, are select to distinguish the splitting directions. Then, the six SVM classifying models are on-line trained at different CU sizes. Finally, the models are utilized to prediction the direction of CU splitting in the quadtree with nested MTT coding structure. Experimental results show that the proposed algorithm can significantly save the encoding time by 51.01% with slight increase of Bjontegaard delta bit rate.

与高效视频编码相比，最新一代的编码标准多功能视频编码（VVC）已实现了更多的比特率降低。但是，引入具有嵌套多类型树（MTT）编码结构的四叉树极大地增加了计算复杂性。为了降低VVC的复杂性，提出了一种基于支持向量机（SVM）的编码单元（CU）大小决策算法。首先，从熵，纹理对比度和当前CU的Haar小波效率中得出有效特征，以区分分裂方向。然后，以不同的CU大小对六个SVM分类模型进行在线训练。最后，利用模型来预测具有嵌套MTT编码结构的四叉树中CU分裂的方向。