A Fast Coding Unit Depth Decision Algorithm for HEVC Inter Prediction

Shan, Nana; Zhou, Wei; Duan, Zhemin; Wei, Henglu

doi:10.1109/fcst.2015.32

“…Numerous information sources are exploited here from directional gradients [79], pyramidal motion divergence [60,61], RD cost of encoding CU [23], etc.However, the most common approach is to determine the texture complexity of the CTU using the variance of pixels since variance is strongly correlated with the texture complexity of the CTU [62,63,65,67,76,80]. Moreover, motion vectors are key factors in determining texture complexity as well, and they are also exploited commonly in these methods [61,73,74,77].…”

Section: Discussionmentioning

confidence: 99%

“…CU complexity is decided by checking the cost of encoding MVs of the current CU and early search termination is done based on a threshold in the method proposed by Shan et al [73].…”

Section: Texture Complexitymentioning

confidence: 99%

CTU depth decision algorithms for HEVC: A survey

Çetinkaya

¹

,

Amirpour

²

,

Ghanbari

³

et al. 2021

Signal Processing: Image Communication

View full text Add to dashboard Cite

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).

show abstract

“…Numerous information sources are exploited here from directional gradients [79], pyramidal motion divergence [60,61], RD cost of encoding CU [23], etc.However, the most common approach is to determine the texture complexity of the CTU using the variance of pixels since variance is strongly correlated with the texture complexity of the CTU [62,63,65,67,76,80]. Moreover, motion vectors are key factors in determining texture complexity as well, and they are also exploited commonly in these methods [61,73,74,77].…”

Section: Discussionmentioning

confidence: 99%

CTU Depth Decision Algorithms for HEVC: A Survey

Cetinkaya,

Amirpour,

Ghanbari

et al. 2021

Preprint

0

View full text Add to dashboard Cite

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).

show abstract