scholarly journals Early CU Depth Decision and Reference Picture Selection for Low Complexity MV-HEVC

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 454 ◽  
Author(s):  
Shahid Khan ◽  
Nazeer Muhammad ◽  
Shabieh Farwa ◽  
Tanzila Saba ◽  
Zahid Mahmood
Keyword(s):  
High Efficiency ◽  
Low Complexity ◽  
High Efficiency Video Coding ◽  
Coding Efficiency ◽  
Temporal Location ◽  
Simulation Results ◽  
Selection For ◽  
The Cost ◽  
Coding Unit ◽  
Base View

The Multi-View extension of High Efficiency Video Coding (MV-HEVC) has improved the coding efficiency of multi-view videos, but this comes at the cost of the extra coding complexity of the MV-HEVC encoder. This coding complexity can be reduced by efficiently reducing time-consuming encoding operations. In this work, we propose two methods to reduce the encoder complexity. The first one is Early Coding unit Splitting (ECS), and the second is the Efficient Reference Picture Selection (ERPS) method. In the ECS method, the decision of Coding Unit (CU) splitting for dependent views is made on the CU splitting information obtained from the base view, while the ERPS method for dependent views is based on selecting reference pictures on the basis of the temporal location of the picture being encoded. Simulation results reveal that our proposed methods approximately reduce the encoding time by 58% when compared with HTM (16.2), the reference encoder for MV-HEVC.

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.

scholarly journals HEVC intra prediction acceleration based on texture direction and prediction unit modes reuse

2014 ◽  
Vol 3 ◽  
Author(s):  
Thaísa Leal da Silva ◽  
Luciano Volcan Agostini ◽  
Luis Alberto da Silva Cruz
Keyword(s):  
High Efficiency ◽  
Prediction Method ◽  
Rate Distortion ◽  
Low Complexity ◽  
Intra Prediction ◽  
High Efficiency Video Coding ◽  
Hierarchical Tree ◽  
The Cost ◽  
Encoding Efficiency ◽  
Hierarchical Tree Structure

The new High Efficiency Video Coding (HEVC) standard achieves higher encoding efficiency when compared to its predecessors such as H.264/AVC. One of the factors responsible for this improvement is the new intra prediction method, which introduces a larger number of prediction directions resulting in an enhanced rate-distortion (RD) performance obtained at the cost of higher computational complexity. This paper proposes an algorithm to accelerate the intra mode decision, reducing the complexity of intra coding. The acceleration procedure takes into account the texture local directionality information and explores the correlation of intra modes across levels of the hierarchical tree structure used in HEVC. Experimental results show that the proposed algorithm provides a decrease of 39.22 and 43.88% in the HEVC intra prediction processing time on average, for all-intra high efficiency (AI-HE) and low complexity (AI-LC) configurations, respectively, with a small degradation in encoding efficiency (BD-PSNR loss of 0.1 dB for AI-HE and 0.8 dB for AI-LC on average).

scholarly journals Detection of Transcoding from H.264/AVC to HEVC Based on CU and PU Partition Types

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1343 ◽  
Author(s):  
Zhenzhen Zhang ◽  
Changbo Liu ◽  
Zhaohong Li ◽  
Lifang Yu ◽  
Huanma Yan
Keyword(s):  
Support Vector Machine ◽  
Video Coding ◽  
High Efficiency ◽  
High Accuracy ◽  
Support Vector ◽  
High Efficiency Video Coding ◽  
Coding Efficiency ◽  
Strong Robustness ◽  
Distinguishing Features ◽  
Coding Unit

High Efficiency Video Coding (HEVC) is a worldwide popular video coding standard due to its high coding efficiency. To make profits, forgers prefer to transcode videos from previous standards such as H.264/AVC to HEVC. To deal with this issue, an efficient method is proposed to expose such transcoded HEVC videos based on coding unit (CU) and prediction unit (PU) partition types. CU and PU partitioning are two unique syntactic units of HEVC that can reflect a video’s compression history. In this paper, CU and PU partition types of I pictures and P pictures are firstly extracted. Then, their mean frequencies are calculated and concatenated as distinguishing features, which are further sent to a support vector machine (SVM) for classification. Experimental results show that the proposed method can identify transcoded HEVC videos with high accuracy and has strong robustness against frame-deletion and shifted Group of Pictures (GOP) structure attacks.

Run-Time Deep Learning Enhanced Fast Coding Unit Decision for High Efficiency Video Coding

2019 ◽  
Vol 29 (03) ◽  
pp. 2050046
Author(s):  
Xin Li ◽  
Na Gong
Keyword(s):  
Deep Learning ◽  
Video Coding ◽  
High Efficiency ◽  
Random Access ◽  
High Efficiency Video Coding ◽  
Decision Algorithm ◽  
Coding Efficiency ◽  
Low Delay ◽  
Cu Partition ◽  
Coding Unit

The state-of-the-art high efficiency video coding (HEVC/H.265) adopts the hierarchical quadtree-structured coding unit (CU) to enhance the coding efficiency. However, the computational complexity significantly increases because of the exhaustive rate-distortion (RD) optimization process to obtain the optimal coding tree unit (CTU) partition. In this paper, we propose a fast CU size decision algorithm to reduce the heavy computational burden in the encoding process. In order to achieve this, the CU splitting process is modeled as a three-stage binary classification problem according to the CU size from [Formula: see text], [Formula: see text] to [Formula: see text]. In each CU partition stage, a deep learning approach is applied. Appropriate and efficient features for training the deep learning models are extracted from spatial and pixel domains to eliminate the dependency on video content as well as on encoding configurations. Furthermore, the deep learning framework is built as a third-party library and embedded into the HEVC simulator to speed up the process. The experiment results show the proposed algorithm can achieve significant complexity reduction and it can reduce the encoding time by 49.65%(Low Delay) and 48.81% (Random Access) on average compared with the traditional HEVC encoders with a negligible degradation (2.78% loss in BDBR, 0.145[Formula: see text]dB loss in BDPSNR for Low Delay, and 2.68% loss in BDBR, 0.128[Formula: see text]dB loss in BDPSNR for Random Access) in the coding efficiency.

A Fast Intra Mode Prediction Decision Algorithm Based on Neighborhood Grouping for HEVC

2019 ◽  
Vol 63 (6) ◽  
pp. 60503-1-60503-13
Author(s):  
Jianhua Wang ◽  
Fujian Xu ◽  
Yanliang Diao ◽  
Jun Liu ◽  
Yubin Lan ◽  
...  
Keyword(s):  
Computational Complexity ◽  
High Efficiency ◽  
Rate Distortion ◽  
Direct Calculation ◽  
Optimal Prediction ◽  
High Efficiency Video Coding ◽  
Decision Algorithm ◽  
Coding Efficiency ◽  
Prediction Mode ◽  
Coding Unit

Abstract High Efficiency Video Coding (HEVC) employs quadtree coding tree unit (CTU) structure to improve its coding efficiency, but at the same time, it requires a very high computational complexity due to its exhaustive calculating process to find the optimal prediction mode for the current CU (Coding Unit). Aiming to solve the problem, a fast intra mode prediction decision algorithm based on neighborhood grouping is presented for HEVC in this article. The contribution of this article lies in the fact that the authors successfully use the neighborhood grouping technology to rapidly find the optimal prediction mode for the current CU, which can significantly reduce computation complexity for HEVC. Specifically, they use the correlative information of adjacent angle prediction mode in their first scheme to quickly reduce the number of Rough Mode Decision (RMD); then they use the correlation between Most Probable Mode (MPM) and the first candidate mode selected as the optimal prediction mode to quickly determine the number of candidate modes; at last, they quickly find the optimal prediction mode with the minimum Rate Distortion Cost (RDCost) by using neighborhood grouping technology or direct calculation approach based on the number of candidate modes. As a result, their proposed algorithm can efficiently solve the problem above. The simulation results show that our proposed intra mode prediction decision algorithm based on neighborhood grouping in this article can reduce about 22.3% computational complexity on average only at a cost of 0.03% bit rate increase and 0.26% PSNR decline compared with the standard reference HM16.1 algorithm.

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 Learned Video Compression via Joint Spatial-Temporal Correlation Exploration

2020 ◽  
Vol 34 (07) ◽  
pp. 11580-11587
Author(s):  
Haojie Liu ◽  
Han Shen ◽  
Lichao Huang ◽  
Ming Lu ◽  
Tong Chen ◽  
...  
Keyword(s):  
Video Coding ◽  
Video Compression ◽  
High Efficiency ◽  
Temporal Correlation ◽  
Second Order ◽  
Compression Method ◽  
High Efficiency Video Coding ◽  
First Order ◽  
Coding Efficiency ◽  
The Common

Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency. Efficient temporal information representation plays a key role in video coding. Thus, in this paper, we propose to exploit the temporal correlation using both first-order optical flow and second-order flow prediction. We suggest an one-stage learning approach to encapsulate flow as quantized features from consecutive frames which is then entropy coded with adaptive contexts conditioned on joint spatial-temporal priors to exploit second-order correlations. Joint priors are embedded in autoregressive spatial neighbors, co-located hyper elements and temporal neighbors using ConvLSTM recurrently. We evaluate our approach for the low-delay scenario with High-Efficiency Video Coding (H.265/HEVC), H.264/AVC and another learned video compression method, following the common test settings. Our work offers the state-of-the-art performance, with consistent gains across all popular test sequences.

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.

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