Fast intra mode decision and fast CU size decision for depth video coding in 3D-HEVC

Chiang, Jui-Chiu; Peng, Kuan-Kai; Wu, Chao-Chun; Deng, Chih-You; Lie, Wen-Nung

doi:10.1016/j.image.2018.10.009

“…is section presents the resulting experiences of the size decision model performance developed for the depth map intraprediction in 3D-HEVC compared with the state-ofthe-art size prediction methods [10][11][12][13][14][15][16]. e anchor HTM-16.2 is used as the reference 3D video coding software [33].…”

Section: Resulting Experiencesmentioning

confidence: 99%

“…Recently, some research studies have been developed to accelerate the size decision process in 3D-HEVC intradepth map coding [10][11][12][13][14][15][16][17] and in HEVC/VVC [18,19]. In [10], the proposed method used a speci c feature called a corner point to accelerate the quadtree intradecision.…”

Section: Introductionmentioning

confidence: 99%

“…e proposed algorithm in [13] exploits the correlation between hierarchical CU/PU and then reduces the number of recursive rate-distortion optimization costs in the depth map intraprediction. Chiang et al [14] develop a low complexity CU size prediction. e smaller CU sizes are not performed if some conditions are satisfied, including the oversplitting of the collocated texture CU.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Computation Complexity Reduction of the Size Decision Algorithm in 3D-HEVC Depth Map Intracoding

Hamout

¹

,

Elyousfi

²

2022

Advances in Multimedia

6

0

View full text Add to dashboard Cite

A new video format called a multiview video plus depth map is recently designed as the most efficient 3D video representation. 3D-high efficiency video coding (3D-HEVC) is the international standard for 3D video coding finalized in 2016. In 3D-HEVC intracoding, the depth map is an essential component, in which the depth map intraprediction occupies more than 85% of the overall intraencoding time. In 3D-HEVC design, a highly flexible quadtree coding unit partitioning is adopted, in which one coding tree unit is partitioned recursively into a prediction unit (PU) from 64 × 64 down to 4 × 4. This highly flexible partitioning provides more accurate prediction signals and thereby achieves better intradepth map compression efficiency. However, performing all depth map intraprediction modes for each PU level achieves high depth map intracoding efficiency, but it results in a substantial computational complexity increase. This paper proposes an amelioration of the previously proposed depth map PU size decision using an efficient homogeneity determination. The resulting experiences show that the proposed method can significantly save the computational complexity with a negligible loss of intracoding 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%

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 intra mode decision and fast CU size decision for depth video coding in 3D-HEVC

Cited by 9 publications

References 22 publications

A Computation Complexity Reduction of the Size Decision Algorithm in 3D-HEVC Depth Map Intracoding

A Computation Complexity Reduction of the Size Decision Algorithm in 3D-HEVC Depth Map Intracoding

CTU depth decision algorithms for HEVC: A survey

CTU Depth Decision Algorithms for HEVC: A Survey

Contact Info

Product

Resources

About