High-Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The Coding Tree Unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64x64 pixels. Each CTU is then divided recursively into a number of equally sized square areas, known as Coding Units (CUs). Although this diversity of frame partitioning increases encoding efficiency, it also causes an increase in the time complexity due to the increased number of ways to find the optimal partitioning. To address this complexity, numerous algorithms have been proposed to eliminate unnecessary searches during partitioning CTUs by exploiting the correlation in the video. In this paper, existing CTU depth decision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches. Statistics approaches are further subdivided into neighboring and inherent approaches. Neighboring approaches exploit the similarity between adjacent CTUs to limit the depth range of the current CTU, while inherent approaches use only the available information within the current CTU. Machine learning approaches try to extract and exploit similarities implicitly. Traditional methods like support vector machines or random forests use manually selected features, while recently proposed deep learning methods extract features during training. Finally, this paper discusses extending these methods to more recent video coding formats such as Versatile Video Coding (VVC) and AOMedia Video 1(AV1).

中文翻译：

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HEVC的CTU深度决策算法：调查

高效视频编码（HEVC）通过引入新的编码工具以增加编码时间复杂度为代价，在编码效率方面超越了其前身。编码树单元（CTU）是HEVC中使用的主要构建块。在HEVC标准中，将帧划分为具有最大64x64像素的预定大小的CTU。然后，将每个CTU递归地划分为多个大小相等的正方形区域，称为编码单位（CU）。尽管帧划分的这种多样性提高了编码效率，但是由于找到最佳划分的方法数量增加，它也导致了时间复杂度的增加。为了解决这种复杂性，已经提出了许多算法来通过利用视频中的相关性来消除在分割CTU期间不必要的搜索。在本文中，对现有的用于HEVC的CTU深度决策算法进行了调查。这些算法分为两类，即统计和机器学习方法。统计方法又细分为相邻方法和固有方法。相邻方法利用相邻CTU之间的相似性来限制当前CTU的深度范围，而固有方法仅使用当前CTU中的可用信息。机器学习方法尝试隐式提取和利用相似性。支持向量机或随机森林等传统方法使用手动选择的特征，而最近提出的深度学习方法则是在训练过程中提取特征。最后，本文讨论了将这些方法扩展到最新的视频编码格式，例如通用视频编码（VVC）和AOMedia Video 1（AV1）。