Fast CU Depth Decision for HEVC Using Neural Networks

Kim, Kyungah; Ro, Won Woo

doi:10.1109/tcsvt.2018.2839113

Cited by 59 publications

(43 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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: 81%

“…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 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. However, the proposed method is based on the maximum entropy model.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…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. In this context, a database is developed, which includes image values and encoding information.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

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

2020

View full text Add to dashboard Cite

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.

show abstract

“…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: 81%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

2020

View full text Add to dashboard Cite

show abstract

“…Statistical analysis of the RD cost can also be used for fast CU size decisions [9], the higher the value is, the higher the probability that the CU judges to split, otherwise, the greater the probability that the CU judges to be early terminated. The convolutional neural network can be used to decide the division of CU more effectively [10][11][12], but it also greatly increases the difficulty of hardware implementation. In the fast mode selection algorithm, the texture direction is adopted mostly.…”

Section: Introductionmentioning

confidence: 99%

Fast algorithm for HEVC intra‐coding implemented by preprocessing

Xu³

et al. 2019

IET Image Processing

View full text Add to dashboard Cite

A Deep CNN-LSTM Framework for Fast Video Coding

Bouaafia¹,

Khemiri²,

Sayadi³

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

High Efficiency Video Coding (HEVC) doubles the compression rates over the previous H.264 standard for the same video quality. To improve the coding efficiency, HEVC adopts the hierarchical quadtree structured Coding Unit (CU). However, the computational complexity significantly increases due to the full search for Rate-Distortion Optimization (RDO) to find the optimal Coding Tree Unit (CTU) partition.Here, this paper proposes a deep learning model to predict the HEVC CU partition at inter-mode, instead of brute-force RDO search. To learn the learning model, a large-scale database for HEVC inter-mode is first built. Second, to predict the CU partition of HEVC, we propose as a model a combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. The simulation results prove that the proposed scheme can achieve a best compromise between complexity reduction and RD performance, compared to existing approaches.

show abstract

Fast CU Depth Decision for HEVC Using Neural Networks

Cited by 59 publications

References 20 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

Fast algorithm for HEVC intra‐coding implemented by preprocessing

A Deep CNN-LSTM Framework for Fast Video Coding

Contact Info

Product

Resources

About