通过时空相关性优化几何分割，减少视频编码侧信息开销。
Spatio-Temporal Correlation Guided Geometric Partitioning for Versatile Video Coding
- 利用边缘和历史模式预测高概率分割方式，降低编码复杂度。
- 运动信息用自适应候选列表索引表示，平均节省0.95%~1.98%码率。
- 适合需要高效压缩的实时视频传输与低延迟场景应用。
几何分割因其在混合视频编码框架中出色的运动场描述能力而受到关注。然而，当前在通用视频编码（VVC）中的几何分割（GEO）方案对侧信息的编码带来了显著负担，限制了编码效率。为此，本文提出一种时空相关性引导的几何分割（STGEO）方案，以更高效地描述视频编码中的对象信息。该方法从统计角度分析了分割模式决策与运动矢量选择特性，基于观测到的时空相关性，设计了模式预测与编码机制，从而减少分割模式和运动信息的信号开销。核心思想是预测高概率的STGEO模式和运动候选，指导熵编码，使高频模式与运动候选以更少比特表示。具体而言，高概率的STGEO模式根据邻近已编码块的边缘信息与历史模式进行预测；运动信息则通过自适应推断的合并候选选择概率生成的候选列表索引表示。仿真结果表明，与VTM-8.0无GEO配置相比，所提方法在随机访问和低延迟B配置下分别实现平均0.95%和1.98%的码率节省。
原文摘要 · Abstract (English)
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.
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