Research Repository

CTU Depth Decision Algorithms for HEVC: A Survey

Çetinkaya, Ekrem and Amirpour, Hadi and Ghanbari, Mohammad and Timmerer, Christian (2021) CTU Depth Decision Algorithms for HEVC: A Survey. Working Paper. arXiv.

[img]
Preview
Text
2104.08328v1.pdf - Submitted Version

Download (15MB) | Preview

Abstract

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).

Item Type: Monograph (Working Paper)
Additional Information: 25 pages, 12 figures
Uncontrolled Keywords: cs.MM, cs.MM, A.1; E.4
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 18 May 2021 12:44
Last Modified: 18 May 2021 12:44
URI: http://repository.essex.ac.uk/id/eprint/30293

Actions (login required)

View Item View Item