Fast intra coding unit decision for high efficiency video coding based on statistical information

2017 ◽  
Vol 55 ◽  
pp. 121-129 ◽  
Author(s):  
Dokyung Lee ◽  
Jechang Jeong
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Statistical Information ◽  
High Efficiency Video Coding ◽  
Intra Coding ◽  
Coding Unit

scholarly journals Adaptive CU Split Decision Based on Deep Learning and Multifeature Fusion for H.266/VVC

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jinchao Zhao ◽  
Yihan Wang ◽  
Qiuwen Zhang
Keyword(s):  
Deep Learning ◽  
Video Coding ◽  
High Efficiency ◽  
Texture Classification ◽  
Rate Distortion ◽  
Classification Model ◽  
High Efficiency Video Coding ◽  
Fast Encoding ◽  
Training Samples ◽  
Coding Unit

With the development of technology, the hardware requirement and expectations of user for visual enjoyment are getting higher and higher. The multitype tree (MTT) architecture is proposed by the Joint Video Experts Team (JVET). Therefore, it is necessary to determine not only coding unit (CU) depth but also its split mode in the H.266/Versatile Video Coding (H.266/VVC). Although H.266/VVC achieves significant coding performance on the basis of H.265/High Efficiency Video Coding (H.265/HEVC), it causes significantly coding complexity and increases coding time, where the most time-consuming part is traversal calculation rate-distortion (RD) of CU. To solve these problems, this paper proposes an adaptive CU split decision method based on deep learning and multifeature fusion. Firstly, we develop a texture classification model based on threshold to recognize complex and homogeneous CU. Secondly, if the complex CUs belong to edge CU, a Convolutional Neural Network (CNN) structure based on multifeature fusion is utilized to classify CU. Otherwise, an adaptive CNN structure is used to classify CUs. Finally, the division of CU is determined by the trained network and the parameters of CU. When the complex CUs are split, the above two CNN schemes can successfully process the training samples and terminate the rate-distortion optimization (RDO) calculation for some CUs. The experimental results indicate that the proposed method reduces the computational complexity and saves 39.39% encoding time, thereby achieving fast encoding in H.266/VVC.

scholarly journals Bagged Tree Based Frame-Wise Beforehand Prediction Approach for HEVC Intra-Coding Unit Partitioning

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1523
Author(s):  
Yixiao Li ◽  
Lixiang Li ◽  
Yuan Fang ◽  
Haipeng Peng ◽  
Yixian Yang
Keyword(s):  
High Efficiency ◽  
High Efficiency Video Coding ◽  
Time Saving ◽  
Intra Coding ◽  
Fast Approach ◽  
Brute Force Approach ◽  
Adaptive Threshold Determination ◽  
Rate Loss ◽  
Prediction Approach ◽  
Coding Unit

High Efficiency Video Coding (HEVC) has achieved about 50% bit-rates saving compared with its predecessor H.264 standard, while the encoding complexity increases dramatically. Due to the introduction of more flexible partition structures and more optional prediction directions, HEVC takes a brute force approach to find the optimal partitioning result which is much more time consuming. Therefore, this paper proposes a bagged trees based fast approach (BTFA) and focuses on the coding unit (CU) size decision for HEVC intra-coding. First, several key features of a target CU are extracted for three-output classifiers. Then, to avoid feature extraction and prediction time over head, our approach is designed frame-wisely, and the procedure is applied parallel with the encoding process. Using the adaptive threshold determination algorithm, our approach achieves 42.04% time saving with negligible 0.92% Bit-Distortion (BD)-rate loss. Furthermore, in order to calculate the optimal thresholds to balance BD-rate loss and complexity reduction, the neural network based mathematical fitting is added to BTFA, which is called the advanced bagged trees based fast approach (ABTFA). Finally, experimental results show that ABTFA achieves 47.87% time saving with only 0.96% BD-rate loss, which outperforms other state-of-the-art approaches.

A Fast Mode Decision Algorithm for Intra Prediction in High Efficiency Video Coding

2020 ◽  
Vol 10 (2) ◽  
pp. 496-501
Author(s):  
Wen Si ◽  
Qian Zhang ◽  
Zhengcheng Shi ◽  
Bin Wang ◽  
Tao Yan ◽  
...  
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Rate Increase ◽  
Intra Prediction ◽  
High Efficiency Video Coding ◽  
Candidate List ◽  
Decision Algorithm ◽  
Coding Efficiency ◽  
Gradient Based ◽  
Coding Unit

High Efficiency Video Coding (HEVC) is the next generation video coding standard. In HEVC, 35 intra prediction modes are defined to improve coding efficiency, which result in huge computational complexity, as a large number of prediction modes and a flexible coding unit (CU) structure is adopted in CU coding. To reduce this computational burden, this paper presents a gradient-based candidate list clipping algorithm for Intra mode prediction. Experimental results show that the proposed algorithm can reduce 29.16% total encoding time with just 1.34% BD-rate increase and –0.07 dB decrease of BD-PSNR.

A context-adaptive fast intra coding algorithm of high-efficiency video coding (HEVC)

2016 ◽  
Vol 16 (4) ◽  
pp. 883-899 ◽  
Author(s):  
Ismail Marzuki ◽  
Jonghyun Ma ◽  
Yong-Jo Ahn ◽  
Donggyu Sim
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
High Efficiency Video Coding ◽  
Intra Coding

scholarly journals Decision tree accelerated CTU partition algorithm for intra prediction in versatile video coding

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258890
Author(s):  
Guowei Teng ◽  
Danqi Xiong ◽  
Ran Ma ◽  
Ping An
Keyword(s):  
Decision Tree ◽  
Video Coding ◽  
High Efficiency ◽  
Intra Prediction ◽  
Image Features ◽  
Test Model ◽  
High Efficiency Video Coding ◽  
Partition Structure ◽  
Partition Process ◽  
Coding Unit

Versatile video coding (VVC) achieves enormous improvement over the advanced high efficiency video coding (HEVC) standard due to the adoption of the quadtree with nested multi-type tree (QTMT) partition structure and other coding tools. However, the computational complexity increases dramatically as well. To tackle this problem, we propose a decision tree accelerated coding tree units (CTU) partition algorithm for intra prediction in VVC. Firstly, specially designated image features are extracted to characterize the coding unit (CU) complexity. Then, the trained decision tree is employed to predict the partition results. Finally, based on our newly designed intra prediction framework, the partition process is early terminated or redundant partition modes are screened out. The experimental results show that the proposed algorithm could achieve around 52% encoding time reduction for various test video sequences on average with only 1.75% Bjontegaard delta bit rate increase compared with the reference test model VTM9.0 of VVC.

Sign in / Sign up

Export Citation Format

Share Document