Caixia Bai, Chun Yuan · 2013
The Quad-tree structure in HEVC contributes to significant coding efficiency improvement, especially for high resolution videos. However, its recursive representation increases the coding complexity as well. This paper brings forward a fast coding tree uint algorithm to speed up the original intra coding in HEVC. By analyzing the texture of the CU, to filter out some unnecessary CU candidates. In the paper, two variables are used to evaluate the texture of the CU, that is the sobel gradient and MAD value. Combined these two values, encoding of some CUs can be skipped. Experimental results show that the algorithm obtains around 30% of time reduction with nearly unchanged PSNR and bitrate stays almost the same compared with the original HEVC intra coding.
Cited by 11
Zhaoqing Pan, Sam Kwong, Yun Zhang et al. · 2014
High Efficiency Video Coding (HEVC) is the latest video coding standard, which adapts quadtree structure based Coding Tree Unit (CTU) to improve the coding efficiency. In HEVC encoding process, the CTU is recursively partitioned into coding units according to the quadtree depth. This technique increases the coding efficiency of HEVC, however, the achieved coding efficiency comes at the cost of high computational complexity. In this paper, we propose a fast C-TU quadtree depth decision algorithm to reduce the computational complexity of HEVC. Firstly, based on the best C-TU depth correlation among spatial and temporal neighboring CTUs, an early quadtree depth 0 decision algorithm is proposed. Then, according to the correlation between the prediction unit mode and the best CTU depth selection, a quadtree depth 3 skipped decision algorithm is proposed. Experimental results show that the proposed algorithm can achieve 40% on average encoding time saving, while maintaining a comparable rate-distortion performance.
Cited by 14
Kiho Choi · Optical Engineering · 2012
A fast coding unit (CU) decision method is proposed for high efficiency video coding (HEVC) by determining early the CU sizes based on coding tree pruning. One of the most effective, a newly introduced concept in HEVC, is variable CU size. In determining the best CU size, the reference encoder of the HEVC tests every possible CU size in order to estimate the coding performance of each CU defined by the CU size. This causes major computational complexity within the encoding process, which should be overcome for the implementation of a fast encoder. A simple tree-pruning algorithm is proposed that exploits the observation where the subtree computations can be skipped if the coding mode of the current node is sufficient (e.g., SKIP mode). The experimental results show that the proposed method was able to achieve a 40% reduction in encoding time compared to the HEVC test model 3.0 encoder with only a negligible loss in coding performance. The proposed method was adopted in HEVC test model 4.0 encoder at JCT-VC 6th meeting.
Cited by 66Open access
Suhas Shankarnahalli Krishnegowda, Hosanna Periapandi, J Zhao et al. · International journal of intelligent engineering and systems · 2023
High-efficiency video coding (HEVC) stands as one of the most extensively employed video coding standards, with the primary encoding challenge revolving around the searching process.To minimize coding complexity of HEVC block partition, a new algorithm is proposed for supporting the partition architecture of coding tree unit (CTU) in intra-coding.In this research, problem of partition architecture decisions of CTU is resolved through two stages.In first phase, the bagged tree method is evaluated for predicting CTU splitting, and in the second phase, the partition issue of 32 32 size CU is given as 17 outcome classification.To attain a huge accuracy of prediction, a residual attention-based long short-term memory (LSTM) with parametric rectified linear unit (AT-LSTM-PReLU) is proposed.The proposed method produces a structure of partition quad-tree of CTU that makes multiple decisions at variant depth levels.The performance of proposed method is estimated with a dataset gathered from the joint collaborative team and video coding (JCT-VC).The proposed approach achieved a remarkable bit rate improvement of 3.72%, a significant T enhancement of 97.28%, and minimized video quality degradation by just -0.05dB.These results outperform other deep learning methods like convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM).
Cited by 1Open access
Shin Kim, Yegi Lee, Kyoungro Yoon · IEEE Access · 2023
Image analysis based on machine vision is hugely manipulated in the smart industry. Good-quality images are required for outstanding machine analysis results, but handling high-definition images could be problematic in a constrained environment such as a low-bandwidth network or low-capacity storage. Lowering the image resolution might be a straightforward solution for reducing image data, but it would cause much information loss, leading to the deterioration of machine vision. Moreover, human supervision could be necessary for a contingency that machine vision cannot control. Therefore, an innovative image compression method considering machine and human vision is required; more compression efficiency than the state-of-the-art codec, praiseworthy machine vision performance, and human-recognizable quality. In this paper, we propose Versatile video coding(VVC) based image compression for hybrid vision, i.e., machine vision and human vision. Our work provides a coding tree unit(CTU) level image compression with dual quantization parameters (QPs) according to the quantization parameter map and the saliency extracted by the object detection network; in the salient region, the proposed method maintains high quality with low QP but degrades the quality with high QP in the non-salient region. Compared with VVC, the proposed compression method achieves a bitrate reduction of up to 25.5% in machine vision tasks, proving more compression efficiency and still admirable machine vision performance. From the perspective of human vision, the proposed method provides human-perceptible image quality, preserving acceptable objective quality values.
Cited by 3Open access
Yixiao Li, Lixiang Li, Yuan Fang et al. · Electronics · 2020
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.
Cited by 6Open access
Fangmei Liu, Jiyuan Wang, Qiuwen Zhang · Electronics · 2024
Video encoding technology is a foundational component in the advancement of modern technological applications. The latest standard in universal video coding, H.266/VVC, features a quad-tree with nested multi-type tree (QTMT) partitioning structure, which represents an improvement over its predecessor, High-Efficiency Video Coding (H.265/HEVC). This configuration facilitates adaptable block segmentation, albeit at the cost of heightened encoding complexity. In view of the aforementioned considerations, this paper puts forth a deep learning-based approach to facilitate CU partitioning, with the aim of supplanting the intricate CU partitioning process observed in the Versatile Video Coding Test Model (VTM). We begin by presenting the Gradient Feedback Hierarchical CNN (GFH-CNN) model, an advanced convolutional neural network derived from the ResNet architecture, enabling the extraction of features from 64 × 64 coding unit (CU) blocks. Following this, a hierarchical network diagram (HND) is crafted to depict the delineation of partition boundaries corresponding to the various levels of the CU block’s layered structure. This diagram maps the features extracted by the GFH-CNN model to the partitioning at each level and boundary. In conclusion, a LightGBM-based decision tree classification model (L-DT) is constructed to predict the corresponding partition structure based on the prediction vector output from the GFH-CNN model. Subsequently, any errors in the partitioning results are corrected in accordance with the encoding constraints specified by the VTM, which ultimately determines the final CU block partitioning. The experimental results demonstrate that, in comparison with VTM-10.0, the proposed algorithm achieves a 48.14% reduction in complexity with only a 0.83% increase in bitrate under the top-three configuration, which is negligible. In comparison, the top-two configuration resulted in a higher complexity reduction of 63.78%, although this was accompanied by a 2.08% increase in bitrate. These results demonstrate that, in comparison to existing solutions, our approach provides an optimal balance between encoding efficiency and computational complexity.
Cited by 0Open access
K. Kawamura, Hirokazu Kato, Seiji Naito · 2012
HEVC (High Efficiency Video Coding) is an ongoing standardization target as the next generation of video compression technology. HEVC employs a coding tree block, which is a quad-tree structure of a coding unit. It also employs some unit types; coding unit, prediction unit, and transform unit. A coding unit can be divided into smaller units as prediction units. Though an asymmetric unit is used for inter coding, only symmetric units are permitted for intra coding. In this paper, we propose an asymmetric partitioning with a non-power-of-two transform as a prediction and transform unit. While conventional partitioning locates the cross-point of partitioning lines at the center of the coding unit, the proposed method locates the cross-point in places except center. The proposed method reduces 2.0% BD-bitrate compared with HM5.0 under all intra / high efficiency condition. The validity of the proposed method is confirmed by some experimental results.
Cited by 1
Rujun Wei, Rong Xie, Liang Zhang et al. · 2015
High Efficiency Video Coding (HEVC), the latest video coding standard, achieves significantly better compression performance than the previous coding standards, especially for high resolution (2K, 4K, etc.) video coding. Intra frame coding adopts a flexible quad-tree coding block structure to get the best coding unit partition, which results in extremely high computational complexity. In this paper, a fast depth decision algorithm with enlarged block sizes is proposed to speed up HEVC intra coding for 4K Ultra-HD video. Firstly, sizes of Coding Unit (CU), Prediction Unit (PU) and Transform Unit (TU) are enlarged in the modified Coding Tree Unit structure to reduce coding complexity. Then, gradient information of an image is exploited for early CU termination to optimize CU partition process. Experimental results of several 4K video sequences show that the proposed algorithm can achieve about 56% encoding time reduction compared to HEVC reference software while maintaining the same encoding performance.
Cited by 3
Yuan Gao, Pengyu Liu, Yueying Wu et al. · PLoS ONE · 2016
In high efficiency video coding (HEVC), coding tree contributes to excellent compression performance. However, coding tree brings extremely high computational complexity. Innovative works for improving coding tree to further reduce encoding time are stated in this paper. A novel low complexity coding tree mechanism is proposed for HEVC fast coding unit (CU) encoding. Firstly, this paper makes an in-depth study of the relationship among CU distribution, quantization parameter (QP) and content change (CC). Secondly, a CU coding tree probability model is proposed for modeling and predicting CU distribution. Eventually, a CU coding tree probability update is proposed, aiming to address probabilistic model distortion problems caused by CC. Experimental results show that the proposed low complexity CU coding tree mechanism significantly reduces encoding time by 27% for lossy coding and 42% for visually lossless coding and lossless coding. The proposed low complexity CU coding tree mechanism devotes to improving coding performance under various application conditions.
Cited by 2Open access
Yi‐Sheng Ciou, Mei‐Juan Chen, Chia-Hung Yeh et al. · 2022 IEEE International Conference on Consumer Electronics - Taiwan · 2022
Versatile Video Coding (H.266/VVC), the latest international video coding standard, effectively encodes ultra-high-definition video. Compared to the quadtree (QT) split of High Efficiency Video Coding (H.265/HEVC), the quadtree with nested multi-type tree (QT-MTT) structure applying binary tree (BT) and ternary tree (TT) coding blocks in H.266/VVC provides various coding unit (CU) partitions. The functionality improves the compression performance but greatly increases encoding complexity. Therefore, the acceleration of encoding is an essential issue. In this paper, the image feature extracted by edge detection is utilized to make fast decisions about the TT split and direction in the MTT structure of CU. Moreover, the maximum partition depth of the coding tree unit is predicted from the reference frames for the early termination of the encoding process. Experimental results demonstrate that the coding time of the proposed algorithm is reduced while the bitrate is well-maintained under random access configuration. The proposed method significantly outperforms the previous work.
Cited by 8
Lin, Yih-Chuan, Lai, Jian-Cheng · Redalyc (Universidad Autónoma del Estado de México) · 2015
"High Efciency Video Coding (HEVC), which is the newest video coding standard, has been developed for the efcient compression of ultra high denition videos. One of the important features in HEVC is the adoption of a quad-tree based video coding structure, in which each incoming frame is represented as a set of non-overlapped coding tree blocks (CTB) by variable-block sized prediction and coding process. To do this, each CTB needs to be recursively partitioned into coding unit (CU), predict unit (PU) and transform unit (TU) during the coding process, leading to a huge computational load in the coding of each video frame. This paper proposes to extract visual features in a CTB and uses them to simplify the coding procedure by reducing the depth of quad-tree partition for each CTB in HEVC intra coding mode. A measure for the edge strength in a CTB, which is dened with simple Sobel edge detection, is used to constrain the possible maximum depth of quad-tree partition of the CTB. With the constrained partition depth, the proposed method can reduce a lot of encoding time. Experimental results by HM10.1 show that the average time-savings is about 13.4% under the increase of encoded BD-Rate by only 0.02%, which is a less performance degradation in comparison to other similar methods."
Cited by 0
LIU Yue-hong, NIU Shao-hua, SHEN Xian-hao · SHILAP Revista de lepidopterología · 2022
In order to improve the performance of virtual reality video intraframe prediction coding,convolutional neural network algorithm is used to select video frame coding unit(CU) to reduce the complexity of video image coding.Firstly,quantization parameters are set to obtain the virtual reality video frame samples,then the image coding tree is constructed,and the convolutional neural network (CNN) frame coding unit optimization model is established.The image brightness of frame samples is taken as the CNN input,combined with the image rate distortion cost threshold,the optimization results of the frame coding unit are obtained through training.Using CNN training optimization,the coding tree(CTU) structure with different depths and an appro-priate number of CU modules can be obtained according to the intraframe coding requirements of different texture modules of the image.Experiments show that,by reasonably setting the convolution kernel size and quantization parameters,CNN algorithm can obtain better image quality and less coding time than common video intraframe prediction coding algorithms.
Cited by 0Open access
Xin Lu, Nan Xiao, Yue Hu et al. · 2016
HEVC employs a quad-tree based Coding Unit (CU) structure to achieve a significant improvement in coding efficiency compared with previous standards. However, the computational complexity is greatly increased. We proposed a fast mode decision algorithm to reduce intra coding complexity. Firstly, an initial candidate list of intra modes is constructed for each Prediction Unit (PU). The prediction mode correlation between adjacent quad-tree coding levels and between temporal neighbouring frames is used to predict the most likely coding mode. The number of prediction mode that need to be evaluated in residual quad-tree (RQT) process is further reduced by taking the Hadamard cost of prediction mode into consideration. Simulation results show that the proposed algorithm saves encoding time by up to 51% compared with the HM 13.0 implementation, while having a negligible impact on rate distortion.
Cited by 8
Yih Chuan Lin, Jian Cheng Lai · 2014
High Efficiency Video Coding (HEVC) is a new generation video coding standard by Joint Collaborative Team on Video Coding (JCTVC). The development of HEVC aims at the compression for ultra high definition videos. In HEVC, the encoder adopts a quad-tree based video coding structure, in which each incoming frame is split into a set of coding tree blocks (CTB) for compression. Each CTB can be recursively split into coding unit (CU), predict unit (PU) and transform unit (TU). This paper proposes an early termination algorithm of recursive partition based on the Sobel edge density of CTB in HEVC intra coding mode. The proposed method can reduce the depth of recursive partition for each CTB and thus save a lot of encoding time. Experimental results by HM10.1 show that the average time savings is about 14.2% and the resultant increase of encoded BD-Rate is only 0.02%. The proposed method can achieve significant time savings with negligible BD-Rate increase.
Cited by 16
Bouthaina Abdallah, Fatma Belghith, Mohamed Ali Ben Ayed et al. · Journal of Electronic Imaging · 2022
Versatile Video Coding (VVC) is the latest generation of the video coding standard. In VVC, the advanced quadtree with a nested multitype tree (QTMT) partition structure provides more flexible coding unit (CU) partition sizes compared with the quadtree (QT) decision tree structure applied in the previous High Efficiency Video Coding (HEVC) standard. This flexibility, achieved by the new QTMT partitioning improvement, considerably improves the coding performance while increasing the coding computational complexity caused mainly by the rate distortion optimization processing. To overcome the complexity issue, a fast deep intra QTMT decision tree approach based on a convolution neural network (CNN) is adopted to determine the QTMT depth decision of each 128 × 128 Coding Tree Unit (CTU). The proposed algorithm predicts both the BT depths at 32 × 32 CUs and the QT depths at 64 × 64 using trained CNNs designed for each structure instead of processing the RDcost. Experimental results prove that the suggested deep QTMT approach achieves an important complexity reduction of up to 55.51% compared with the original reference software VTM3.0, with an average of about 35% encoding time reduction accompanied by an insignificant loss in encoding performance.
Cited by 3Open access
Yu‐Wen Huang, Chih‐Wei Hsu, Ching-Yeh Chen et al. · IEEE Transactions on Circuits and Systems for Video Technology · 2019
This paper describes a video coding technology proposal submitted by MediaTek in April 2018 in response to a joint call for proposals (CfP) issued by ITU-T VCEG and ISO/IEC MPEG. The proposal uses the conventional block-based hybrid coding approach with a breakthrough design of block structure, substantial improvements of coding tools, and new inventions of loop filtering techniques. First, the block structure is designed using a highly flexible partitioning scheme, where each coding tree unit in a picture is split into multiple coding units (CUs) by a recursive quaternary tree followed by a recursive binary-ternary tree. The prediction unit (PU) and transform unit (TU) concepts are unified with the CU concept, i.e., PU and TU are basically the same as CU. Moreover, inter prediction, intra prediction, transform, quantization, in-loop filtering, and entropy coding are all improved. Finally, new loop filtering techniques are invented, where the proposed convolutional neural network loop filtering is of particular interest. The proposed video codec achieves 43.81% average bit rate savings based on objective measures compared to the High Efficiency Video Coding (HEVC) anchors and is among the best-performing CfP responses both objectively and subjectively. It has been partially adopted into the working draft of the Versatile Video Coding (VVC) standard.
Cited by 54
Weijia Zhu, Jizheng Xu, Li Zhang et al. · 2021
The quad-tree plus binary-tree (QTBT) partition structures was adopted into the versatile video coding (VVC) standard. In the QTBT partition structure, a dual tree structure can be applied on intra slices where the partitioning structures for luminance and chrominance components are separate. The dual tree structure is efficient for camera captured videos. However, it may be not the case for screen content videos. Therefore, adaptive dual tree structure is proposed in this paper wherein the coding structure of each coding tree unit is switched between separate and joint coding structure to adapt the textures adaptively. The proposed scheme is implemented into the reference software of the VVC standard-VTM. Simulation results report that up to 4.5% BD bitrate saving can achieve on the typical screen content videos when compared to VTM5.
Cited by 5
Tian‐Wen Xiao, Yong Xu, Lu Jin et al. · 2020
Background. Gene tree discordance is common in phylogenetic analyses. Many phylogenetic studies have excluded non-coding regions of the plastome without evaluating their impact on tree topology. In general, plastid loci have often been treated as a single unit, and tree discordance among these loci has seldom been examined. Using samples of Laureae (Lauraceae) plastomes, we explored plastome variation among the tribe, examined the influence of non-coding regions on tree topology, and quantified intra-plastome conflict.
Cited by 0Open access
Zhao Wang, Shiqi Wang, Jian Zhang et al. · 2017
Block partition structure has been recognized as a crucial module in video coding scheme. Recently, a quadtree plus binary tree (QTBT) block partition structure has been proposed in the Joint Video Exploration Team (JVET) development. Compared to the quadtree structure in HEVC, QTBT can achieve better coding performance with hugely increased encoding complexity. Here, we propose an effective QTBT partition decision algorithm to achieve a good trade-off between computational complexity and coding performance. In particular, at the Coding Tree Unit level, the partition parameters of QTBT are dynamically derived to adapt to the local characteristics without transmitting any overhead. Subsequently, at the Coding Unit level, a joint-classifier decision tree structure is designed to eliminate unnecessary iterations and meanwhile control the risk of false prediction. Experimental results show that the proposed algorithm can achieve 64% encoding time reduction on average with only 1.26% increase in terms of bit rate. This greatly benefits the practical implementations of QTBT in real application scenarios.
Cited by 53