Coding unit splitting early termination for fast HEVC intra coding based on global and directional gradients

Jamali, Mohammadreza; Coulombe, Stéphane

doi:10.1109/mmsp.2016.7813356

“…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%

“…Jamali and Coulombe [79] propose an intra coding method based on the global and directional gradients. Based on the accuracy of the prediction in the current depth, CUs are classified into two categories: split and non-split.…”

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

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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).

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“…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

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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).

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“…Mohammad reza Jamali et al (15) propose a new unit splitting method based on directional and global gradients that enables early unit splitting and avoids extreme depths; this method reduces encoding time by 51.9% on average while sacrificing 0.07dB of quality (BDPSNR).…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Based Low-Complexity andHigh-Speed Coding Unit Partition Decisionfor High Efficiency Video Coding

Pattimi

¹

,

B.K.N

²

2023

Preprint

0

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High-Efficiency Video Coding (HEVC/H.265) is the current video coding standard and it achieves a 50% greater bitrate reduction than H.264.The Coding Tree Unit (CTU) is the main processing unit that definesthe HEVC decoding process. Due to recursive search method used inHEVC encoder to find the Coding Unit (CU) partitions, it increasescomputational complexity and time for processing. This paper suggeststwo approaches for coding unit partitioning, one is based on machinelearning and the other is based on deep learning. The parameterslike Edge Magnitude (EM), Rough Mode Decision cost (RMD), RateDistortion cost (RD), and Most Probable Modes (MPM) are used toestimate the complexity of CU splitting. These parameters are utilised bymachine learning methods, such as Fischer’s Linear Discriminant Analysis (FLDA), Support Vector Machine (SVM), and Logistic Regression(LR), to classify split and un-split samples of the coding unit partitioning. The machine learning algorithms increased the coding efficiencyand decreased its computing complexity in comparison to the traditionalHEVC encoder. However, machine learning algorithms also used the iterative procedure in determining the splitting of the coding units. Thispaper proposes an alternative method using deep learning technique toaddress the recursive search issue. Using a modified version of the LeNet-5 architecture, our proposed deep learning method simplifies coding unitpartitioning because neither recursive searching nor rate-distortion optimization are required to identify the coding unit partitioning in modifiedLeNet-5 architectures. As a result, the coding unit partition speed is raised in all intra-configurations without changing the computational costof HEVC. According to simulation results, the proposed techniques LR,SVM, and modified LeNet-5 could individually reduce nearly 50.09%,63.78%, and 86.06% of the time required to encode various test videos.

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Coding unit splitting early termination for fast HEVC intra coding based on global and directional gradients

Cited by 8 publications

References 12 publications

CTU depth decision algorithms for HEVC: A survey

CTU depth decision algorithms for HEVC: A survey

CTU Depth Decision Algorithms for HEVC: A Survey

Deep Learning Based Low-Complexity andHigh-Speed Coding Unit Partition Decisionfor High Efficiency Video Coding

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