School of Communication and Information Engineering, Shanghai University, Shanghai, China.
PLoS One. 2021 Nov 8;16(11):e0258890. doi: 10.1371/journal.pone.0258890. eCollection 2021.
Versatile video coding (VVC) achieves enormous improvement over the advanced high efficiency video coding (HEVC) standard due to the adoption of the quadtree with nested multi-type tree (QTMT) partition structure and other coding tools. However, the computational complexity increases dramatically as well. To tackle this problem, we propose a decision tree accelerated coding tree units (CTU) partition algorithm for intra prediction in VVC. Firstly, specially designated image features are extracted to characterize the coding unit (CU) complexity. Then, the trained decision tree is employed to predict the partition results. Finally, based on our newly designed intra prediction framework, the partition process is early terminated or redundant partition modes are screened out. The experimental results show that the proposed algorithm could achieve around 52% encoding time reduction for various test video sequences on average with only 1.75% Bjontegaard delta bit rate increase compared with the reference test model VTM9.0 of VVC.
多功能视频编码（VVC）采用四叉树嵌套多类型树（QTMT）分区结构和其他编码工具，相较于先进的高效视频编码（HEVC）标准取得了巨大的性能提升。然而，其计算复杂度也显著增加。为了解决这个问题，我们提出了一种用于 VVC 中预测的决策树加速编码树单元（CTU）分区算法。首先，提取特定的图像特征来描述编码单元（CU）的复杂度。然后，使用训练好的决策树来预测分区结果。最后，基于我们新设计的预测框架，提前终止分区过程或筛选出冗余的分区模式。实验结果表明，与 VVC 的参考测试模型 VTM9.0 相比，所提出的算法能够在各种测试视频序列上平均实现约 52%的编码时间减少，同时仅增加 1.75%的 Bjontegaard 比特率差值。
