Fast Coding Unit Partition Decision for HEVC Using Support Vector Machines

Grellert, Mateus; Bampi, Sérgio; Cruz, Luís A. da Silva

doi:10.1109/tcsvt.2018.2849941

Cited by 46 publications

(34 citation statements)

References 15 publications

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“…Random Access Our work (0.93,−0.036, 56.36) Tai et al [16] (1.41, −0.054, 45.70) Grellert et al [18] (0.48, −0.048, 48.00) Kim et al [19] (1.51, −0.052, 47.32)…”

Section: Approach (Bd-br Bd-psnr ∆T)mentioning

confidence: 82%

“…In summary, the proposed method can significantly reduce the computational complexity under the LD and RA configurations. Table 3 shows the BD-BR, BD-PSNR, and time saving performance in comparison with those of previous works in [16,18,19]. The authors of [16] propose an efficient complexity reduction method that takes early CU split, CU termination, and search range adjustment into account.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The authors of [16] propose an efficient complexity reduction method that takes early CU split, CU termination, and search range adjustment into account. The work in [18] presents a fast CU size decision method which is based on SVM with off-line learning. In [19], a neural-network-based CU partitioning method is proposed.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In this context, Gaussian mixture models are used to estimate statistical parameters. The work in [18] presents a support vector machine (SVM)-based CU size decision algorithm, and a radial basis function (RBF) kernel is used to optimize variables. In [19], a neural-networks-based CU depth decision method is proposed to reduce the complexity of the H.265/HEVC encoder.…”

Section: Related Workmentioning

confidence: 99%

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Maximum-Entropy-Model-Enabled Complexity Reduction Algorithm in Modern Video Coding Standards

2020

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Symmetry considerations play a key role in modern science, and any differentiable symmetry of the action of a physical system has a corresponding conservation law. Symmetry may be regarded as reduction of Entropy. This work focuses on reducing the computational complexity of modern video coding standards by using the maximum entropy principle. The high computational complexity of the coding unit (CU) size decision in modern video coding standards is a critical challenge for real-time applications. This problem is solved in a novel approach considering CU termination, skip, and normal decisions as three-class making problems. The maximum entropy model (MEM) is formulated to the CU size decision problem, which can optimize the conditional entropy; the improved iterative scaling (IIS) algorithm is used to solve this optimization problem. The classification features consist of the spatio-temporal information of the CU, including the rate–distortion (RD) cost, coded block flag (CBF), and depth. For the case analysis, the proposed method is based on High Efficiency Video Coding (H.265/HEVC) standards. The experimental results demonstrate that the proposed method can reduce the computational complexity of the H.265/HEVC encoder significantly. Compared with the H.265/HEVC reference model, the proposed method can reduce the average encoding time by 53.27% and 56.36% under low delay and random access configurations, while Bjontegaard Delta Bit Rates (BD-BRs) are 0.72% and 0.93% on average.

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“…Random Access Our work (0.93,−0.036, 56.36) Tai et al [16] (1.41, −0.054, 45.70) Grellert et al [18] (0.48, −0.048, 48.00) Kim et al [19] (1.51, −0.052, 47.32)…”

Section: Approach (Bd-br Bd-psnr ∆T)mentioning

confidence: 82%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Maximum-Entropy-Model-Enabled Complexity Reduction Algorithm in Modern Video Coding Standards

2020

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“…However, vast differences in video characteristics and the dynamic nature of the video contents make it difficult to maintain a fixed set of rigid rules to determine the optimal CU sizes for particular video content. On the other hand, learning-based approaches often utilise machine learning methods with pre-trained models generated from vast amounts of data from previously encoded sequences [10]- [14]. Yet, the use of fixed rigid models makes them less adaptive to the changing properties (i.e., texture and motion characteristics) of natural video sequences.…”

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confidence: 99%

iCUS: Intelligent CU Size Selection for HEVC Inter Prediction

et al. 2020

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The hierarchical quadtree partitioning of Coding Tree Units (CTU) is one of the striking features in HEVC that contributes towards its superior coding performance over its predecessors. However, the brute force evaluation of the quadtree hierarchy using the Rate-Distortion (RD) optimisation, to determine the best partitioning structure for a given content, makes it one of the most time-consuming operations in HEVC encoding. In this context, this paper proposes an intelligent fast Coding Unit (CU) size selection algorithm to expedite the encoding process of HEVC inter-prediction. The proposed algorithm introduces (i) two CU split likelihood modelling and classification approaches using Support Vector Machines (SVM) and Bayesian probabilistic models, and (ii) a fast CU selection algorithm that makes use of both offline trained SVMs and online trained Bayesian probabilistic models. Finally, (iii) a computational complexity to coding efficiency trade-off mechanism is introduced to flexibly control the algorithm to suit different encoding requirements. The experimental results of the proposed algorithm demonstrate an average encoding time reduction performance of 53.46%, 61.15%, and 58.15% for Low Delay B, Random Access, and Low Delay P configurations, respectively, with Bjøntegaard Delta-Bit Rate (BD-BR) losses of 2.35%, 2.9%, and 2.35%, respectively, when evaluated across a wide range of content types and quality levels. INDEX TERMS Coding Unit (CU), encoder complexity reduction, High Efficiency Video Coding (HEVC), inter-prediction, Support Vector Machine (SVM) R ECENT advancements in multimedia technologies that span across Consumer Electronics (CE) in video content capturing, transmission and display have made video data the most frequently exchanged type of content over the modern communication networks. The increasing mobile consumption of High Definition (HD) and Ultra High Definition (UHD) video contents has contributed immensely towards the ever-growing IP video traffic and it is expected to reach over 82% of the overall Internet traffic in 2021 [1]. However, the estimated growth in network bandwidth (1.9 fold from 2017-2022, which is 39.0 Mbps to 75.4 Mbps for fixed broadband [1]) with time is still not sufficient to cater for the ever-growing user demands. Furthermore, the video requirements for emerging applications such as Augmented Reality (AR)/Virtual Reality (VR), interactive television, multi-party video conferences and over-the-top (OTT) multimedia consumption demand continuous improvements in the compression efficiency [2]. In this regard, High Efficiency Video Coding (HEVC) which was introduced in 2013 is the most recent stable video coding standard. It provides greater compression efficiency through an assortment of new features and coding tools over its predecessor H.264/AVC [3]. Out of these, the hierarchical quadtree partitioning structure introduced in HEVC that entails a wide range of Coding Unit (CU) sizes (i.e., 8 × 8 to 64 × 64) and their combinations, is one of the important contributors...

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Fast Coding Unit Partition Decision for Intra Prediction in Versatile Video Coding

Zhang

Chen

et al. 2021

Lecture Notes in Computer Science

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Fast Coding Unit Partition Decision for HEVC Using Support Vector Machines

Cited by 46 publications

References 15 publications

Maximum-Entropy-Model-Enabled Complexity Reduction Algorithm in Modern Video Coding Standards

Maximum-Entropy-Model-Enabled Complexity Reduction Algorithm in Modern Video Coding Standards

iCUS: Intelligent CU Size Selection for HEVC Inter Prediction

Fast Coding Unit Partition Decision for Intra Prediction in Versatile Video Coding

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