CTU depth decision algorithms for HEVC: A survey

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 64 × 64 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).

CD-LwTE: Cost- and Delay-Aware Light-Weight Transcoding at the Edge

“…For example, HEVC/H.265 compared to AVC/H.264 achieves 50% bitrate savings when encoding videos at the same video quality. In HEVC/H.265 [32], video frames are divided into fixed-size blocks named CTUs with the size of 64 × 64 pixels. Each CTU is then subdivided recursively into 85 Coding Units (CUs), including one 64 × 64 pixels CU, four 32 × 32 pixels CUs, sixteen 16 × 16 pixels CUs, and sixty four 8 × 8 pixels CUs.…”

An efficient video coding with object-bounded motion estimation using cuboid-based variable-sized block partitioning

“…This is due to frame sizes being larger compared to CIF or QCIF formats [7] and also 16 × 16 blocks may not be able to represent the spatial correlations efficiently in HD frames [8]. In contrast, larger block sizes can model spatial redundancy more effectively in high-resolution videos and it can also increase the efficiency and flexibility of the encoder [9].…”