Amestoy Thomas, Mercat Alexandre, Hamidouche Wassim, Menard Daniel, Bergeron Cyril
IEEE Trans Image Process. 2019 Sep 6. doi: 10.1109/TIP.2019.2938670.
Block partition structure is a critical module in video coding scheme to achieve significant gap of compression performance. Under the exploration of the future video coding standard, named Versatile Video Coding (VVC), a new Quad Tree Binary Tree (QTBT) block partition structure has been introduced. In addition to the QT block partitioning defined in High Efficiency Video Coding (HEVC) standard, new horizontal and vertical BT partitions are enabled, which drastically increases the encoding time compared to HEVC. In this paper, we propose a lightweight and tunable QTBT partitioning scheme based on a Machine Learning (ML) approach. The proposed solution uses Random Forest classifiers to determine for each coding block the most probable partition modes. To minimize the encoding loss induced by misclassification, risk intervals for classifier decisions are introduced in the proposed solution. By varying the size of risk intervals, tunable trade-off between encoding complexity reduction and coding loss is achieved. The proposed solution implemented in the JEM-7.0 software offers encoding complexity reductions ranging from 30average for only 0.7% to 3.0% Bjxntegaard Delta Rate (BDBR) increase in Random Access (RA) coding configuration, with very slight overhead induced by Random Forest. The proposed solution based on Random Forest classifiers is also efficient to reduce the complexity of the Multi-Type Tree (MTT) partitioning scheme under the VTM-5.0 software, with complexity reductions ranging from 25% to 61% in average for only 0.4% to 2.2% BD-BR increase.
块划分结构是视频编码方案中的一个关键模块，用于实现显著的压缩性能差距。在对名为通用视频编码（VVC）的未来视频编码标准的探索中，引入了一种新的四叉树二叉树（QTBT）块划分结构。除了高效视频编码（HEVC）标准中定义的QT块划分之外，还启用了新的水平和垂直BT划分，这与HEVC相比大幅增加了编码时间。在本文中，我们提出了一种基于机器学习（ML）方法的轻量级且可调节的QTBT划分方案。所提出的解决方案使用随机森林分类器为每个编码块确定最可能的划分模式。为了最小化误分类引起的编码损失，在所提出的解决方案中引入了分类器决策的风险区间。通过改变风险区间的大小，实现了编码复杂度降低与编码损失之间的可调权衡。在所提出的解决方案在JEM-7.0软件中实现，在随机访问（RA）编码配置下，编码复杂度降低范围从30平均仅0.7%到3.0%的Bjøntegaard Delta比特率（BDBR）增加，随机森林引入的开销非常小。基于随机森林分类器的所提出的解决方案在VTM-5.0软件下对于降低多类型树（MTT）划分方案的复杂度也很有效，平均复杂度降低范围从25%到61%，仅BD-BR增加0.4%到2.2%。
