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 Intra Coding Unit Size Decision Based on Statistical Learning for HEVC

2013 ◽  
Vol 303-306 ◽  
pp. 2107-2111 ◽  
Author(s):  
Cheng Tao Zhou ◽  
Xiang Tian ◽  
Yao Wu Chen
Keyword(s):  
Statistical Learning ◽  
High Efficiency ◽  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Unit Size ◽  
High Efficiency Video Coding ◽  
Block Partitioning ◽  
Decision Algorithm ◽  
Intra Coding ◽  
Coding Unit

High Efficiency Video Coding (HEVC) is an ongoing standard, and it employs the quad-tree block partitioning structure which includes coding unit, prediction unit, and transform unit. This content-adaptive coding tree structure can improve HEVC coding efficiency significantly, but it also consumes large computational complexity. This paper proposed a fast intra coding unit size decision algorithm to reduce the heavy complexity of HEVC encoding. First, the proposed algorithm reduced unit sizes search by using the classifier, which is based on the statistical learning. Second, an early largest unit size decision was designed to skip the checking of unnecessary unit sizes. As compared to the full search algorithm in HEVC reference software, experimental results show that the proposed algorithm achieves 50.4% computation saving on average with 1.83% bit rate increase and 0.070dB peak signal-to-noise ratio loss.

scholarly journals Bit Rate Transcoding for High Efficiency Video Coding

2019 ◽  
Vol 9 (2) ◽  
pp. 581-584
Keyword(s):  
Computational Complexity ◽  
Video Coding ◽  
High Efficiency ◽  
Real Life ◽  
Bit Rate ◽  
High Efficiency Video Coding ◽  
Time Saving ◽  
Compression Performance ◽  
Trade Offs ◽  
Coding Unit

High efficiency video coding (HEVC) has demonstrated a notable increase in compression performance and is taken as a successor to H.264/AVC. Efficient bit rate adaptation algorithms are required to contain the HEVC standard between real life community facilities. A present issue of bit rate transcoding is its high computational complexity which is related with the encoder of a cascaded pixel domain transcoder. This paper gives Top to Bottom (T2B) approach to reduce complexity by using different complexity schemes. Proposed approach is effective in reducing complexity in Coding Unit (CU) optimization level. Coding Unit has been analyzed in T2B Approach. While examining the coding unit information of the input video is turned to account for decreasing the number of evaluation and early terminate the process. For the Prediction Unit (PU) level the units are powerfully chosen contingent upon likelihood of Prediction Unit sizes and co-found input prediction partitioning. By utilizing this approach, complexity scalable bit rate transcoding has achieved. Machine learning approach can be used to control computational complexity. Additionally, the T2B strategy is able to gain a spread on trade-offs in transrating complexity and coding performance. Using T2B approach 15% encoding time saving is accomplished. From this scheme, for the less resolution video 27% time saving has achieved.

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.

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 H.265 Intra-Picture Prediction Acceleration using Low Complexity Model

2019 ◽  
Vol 8 (11) ◽  
pp. 2939-2944
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Low Complexity ◽  
Video Encoding ◽  
High Efficiency Video Coding ◽  
Tree Model ◽  
Time Saving ◽  
Applied Machine Learning ◽  
Additional Burden ◽  
Increasing Demand

H.265 also called High Efficiency Video Coding is the new futuristic international standard proposed by Joint collaboration Team on Video Coding and released in 2013 in the view of constantly increasing demand of video applications. This new standard reduces the bitrate to half as compared to its predecessor H.264 at the expense of huge amount of computational burden on the encoder. In the proposed work we focus on intraprediction phase of video encoding where 33 new angular modes are introduced in addition to DC and Planar mode in order to achieve high quality videos at higher resolutions. We have proposed the use of applied machine learning to HEVC intra prediction to accelerate angular mode decision process. The features used are also low complexity features with minimal computation so as to avoid any additional burden on the encoder. The Decision tree model built is simple yet efficient which is the requirement of the complexity reduction scenario. The proposed method achieves substantial average encoding time saving of 86.59%, with QP values 4,22,27,32 respectively with minimal loss of 0.033 of PSNR and 0.0023 loss in SSIM which makes it suitable for acceptance of High Efficiency Video coding in real time applications

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