  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).
