Spatio-Temporal Correlation Guided Geometric Partitioning for Versatile Video Coding
Spatio-Temporal Correlation Guided Geometric Partitioning for Versatile Video Coding
复制标题
用于多功能视频编码的时空相关引导几何分区
DOI：
10.1109/tip.2021.3126420
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发表时间：
2021
影响因子：
10.6
通讯作者：
Siwei Ma
中科院分区：
文献类型：
--
作者：
Xuewei Meng;Chuanmin Jia;Xinfeng Zhang;Shanshe Wang;Siwei Ma
在混合视频编码框架中，几何分割以其出色的运动场描述能力受到越来越多的关注。然而，通用视频编码（VVC）中现有的几何分割（GEO）方案对侧信息的信令造成了不可忽视的负担。因此，编码效率受到限制。鉴于此，我们提出了一种时空相关引导几何分割（STGEO）方案来有效地描述视频编码中运动场中的目标信息。该方法可以节省侧信息信令所消耗的比特，包括分区方式和运动信息。首先从统计合理的角度分析了分割模式选择和运动矢量选择的特点。基于观测到的时空相关性，我们设计了一种模式预测和编码方法，以减少表示上述侧信息的开销。其主要思想是预测具有较高选择可能性的STGEO模式和候选运动，从而指导熵编码，即用较少的比特表示预测的高概率模式和候选运动。特别是基于相邻STGEO编码块的边缘信息和历史模式来预测高概率的STGEO模式。相应的运动信息由合并候选列表中的索引表示，该列表是基于离线训练的合并候选选择概率自适应推断的。仿真结果表明，与没有GEO的VTM-8.0相比，该方法在随机访问和低延迟B配置下平均节省了0.95%和1.98%的比特率。
Geometric partitioning has attracted increasing attention by its remarkable motion field description capability in the hybrid video coding framework. However, the existing geometric partitioning (GEO) scheme in Versatile Video Coding (VVC) causes a non-negligible burden for signaling the side information. Consequently, the coding efficiency is limited. In view of this, we propose a spatio-temporal correlation guided geometric partitioning (STGEO) scheme to efficiently describe the object information in the motion field of video coding. The proposed method can economize the bits consumed for side information signaling, including the partitioning mode and motion information. We firstly analyze the characteristics of partitioning mode decision and motion vector selection in a statistically-sound way. Based on the observed spatio-temporal correlation, we design a mode prediction and coding method to reduce the overhead for representing the above mentioned side information. The main idea is to predict the STGEO modes and motion candidates that have higher selection possibilities, which can guide the entropy coding, i.e., representing the predicted high-probability modes and motion candidates with fewer bits. In particular, the high-probability STGEO modes are predicted based on the edge information and history modes of adjacent STGEO-coded blocks. The corresponding motion information is represented by the index in a merge candidate list, which is adaptively inferred based on the off-line trained merge candidate selection probability. Simulation results show that the proposed approach achieves 0.95% and 1.98% bit-rate savings on average compared to VTM-8.0 without GEO for Random Access and Low-Delay B configurations, respectively.