High-Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encodingtime-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 predeterminedsize of up to 64×64 pixels . Each CTU is then divided recursively into a number of equally sized square areas, known as Coding Units (CUs) This diversity of frame partitioning increases encoding efficiency but 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 depthdecision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches . This paper discusses extending these methods to more recent videocoding formats such as VVC and AOMedia Video 1(AV1).

Author(s) : Ekrem Çetinkaya, Hadi Amirpour, Mohammad Ghanbari, Christian Timmerer

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Code :
Coursera

Keywords : hevc - coding - ctu - algorithms - complexity -

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