Fast algorithm of coding unit depth decision for HEVC intra coding

Huang, Xiaofeng; Jia, Huizhu; Wei, Kaijin; Liu, Jie; Zhu, Chuang; Lv, Zhengguang; Xie, Don

doi:10.1109/vcip.2014.7051605

“…There is a negligible loss in coding efficiency, with 0-0.04 dB drop in PSNR or 0.02-0.88% increase in bit rate. In addition to the HM implementation, we also compared the proposed algorithm with the state-of-the-art fast CU depth decision scheme for HEVC [2,3,8]. A performance comparison with the algorithms proposed by Min, Shen and Huang is also presented in Table 3.…”

Section: Resultsmentioning

confidence: 99%

“…These are categorised into mainly three types: fast coding depth decision [2][3][4][5][6][7][8], fast prediction mode decision [9][10][11][12][13][14][15][16][17][18][19] and hybrid fast scheme [20][21][22][23][24][25][26]. Some typical algorithms in each category are described as follows:…”

Section: Related Workmentioning

confidence: 99%

A fast HEVC intra-coding algorithm based on texture homogeneity and spatio-temporal correlation

Lu

¹

,

Yu

²

,

Xuesong

³

2018

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

The high efficiency video coding (HEVC) standard supports a flexible coding tree unit (CTU) partitioning structure, and thus coding efficiency is improved significantly. However, the use of this technique inevitably results in greatly increased encoding complexity. In order to reduce the complexity of intra-coding, we propose a hybrid scheme consisting of fast coding unit (CU) size decision and fast prediction unit (PU) mode decision processes. An adaptive method is utilised to measure the homogeneity of video content thus avoiding unnecessary rate distortion (RD) evaluations. The depth range to be tested is narrowed based on the partitioning parameters of the spatially adjacent CUs and the temporally co-located CU. Furthermore, the mode correlation between neighbouring frames and between adjacent coding levels in the same quad-tree structure is taken into account to predict the most probable directional mode. The number of candidate PU modes is further decreased according to the Hadamard cost. Experimental results illustrate that our scheme achieves a significant reduction in computational complexity of HEVC intra-coding. Compared with the HM encoder, the encoding time is reduced by up to 71% with negligible degradation in coding efficiency.

show abstract

“…This is useful since more complex CTUs tend to have larger depths to achieve better motion compensation. 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%

“…Huang et al [67] use CU texture complexity along with spatially neighboring CTU depth information. It is calculated by quantizing the variance of the CU into five category levels.…”

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

“…This is useful since more complex CTUs tend to have larger depths to achieve better motion compensation. 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

Fast algorithm of coding unit depth decision for HEVC intra coding

Cited by 10 publications

References 8 publications

A fast HEVC intra-coding algorithm based on texture homogeneity and spatio-temporal correlation

A fast HEVC intra-coding algorithm based on texture homogeneity and spatio-temporal correlation

CTU depth decision algorithms for HEVC: A survey

CTU Depth Decision Algorithms for HEVC: A Survey

Contact Info

Product

Resources

About