The Versatile Video Coding (VVC) standard, introduced in 2020, offers 40-50% bitrate savings for equivalent visual quality of reconstructed videos over its predecessor, High Efficiency Video Coding (HEVC), at the cost of significantly increased encoding complexity. This growth in encoding complexity is mainly due to the addition of the Quad Tree Multi Type Tree (QTMTT) partitioning structure, which increases the split combinatorial complexity. This paper presents a critical evaluation of state-of-the-art (SOTA) partitioning acceleration techniques designed to reduce the complexity of the partitioning search in VVC. Particular attention is given to how these methods have evolved alongside successive versions of the VVC Test Model (VTM), which serves as the reference software for benchmarking coding tools. These techniques are analyzed in the context of their adaptation to internal changes in VTM, such as updated heuristics for fast partitioning decisions. The study also highlights the challenges involved in improving the trade-off between encoding complexity and compression efficiency. This challenge becomes more pronounced when evaluating methods across diverse VTM configurations and multiple software versions.

翻译：2020年发布的通用视频编码(VVC)标准相较于其前身高效率视频编码(HEVC)，在重建视频等效视觉质量条件下可实现40-50%的码率节省，但代价是编码复杂度显著提升。这种编码复杂度的增长主要源于四叉树多类型树(QTMTT)划分结构的引入，该结构增加了分割组合的复杂度。本文对旨在降低VVC中划分搜索复杂度的最先进(SOTA)划分加速技术进行了批判性评估。特别关注这些方法如何随VVC测试模型(VTM)的连续版本共同演进（VTM作为编码工具基准测试的参考软件）。这些技术在其对VTM内部变更的适应性背景下被分析，例如快速划分决策中启发式策略的更新。研究还强调了改善编码复杂度与压缩效率之间权衡所面临的挑战。当在不同VTM配置和多版本软件环境下评估方法时，这一挑战变得尤为突出。