The latest video coding standard, versatile video coding (VVC), achieves almost twice coding efficiency compared to its predecessor, the high efficiency video coding (HEVC). However, achieving this efficiency (for int...
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The latest video coding standard, versatile video coding (VVC), achieves almost twice coding efficiency compared to its predecessor, the high efficiency video coding (HEVC). However, achieving this efficiency (for intracoding) requires 31 x computational complexity compared to HEVC, which makes it challenging for low power and real-time applications. This paper, proposes a novel machine learning approach that jointly and separately employs two modalities of features, to simplify the intracoding decision. To do so, first a set of features are extracted that use the existing DCT core of VVC, to assess the texture characteristics, and forms the first modality of data. This produces high-quality features with almost no extra computational overhead. The distribution of intra modes at the neighboring blocks is also used to form the second modality of data, which provides statistical information about the frame, unlike the first modality. Second, a two-step feature reduction method is designed that reduces the size of feature set, such that a lightweight model with a limited number of parameters can be used to learn the intra mode decision task. Third, three separate training strategies are proposed (1) an offline training strategy using the first (single) modality of data, (2) an online training strategy that uses the second (single) modality, and (3) a mixed online-offline strategy that uses bimodal learning. Finally, a low-complexity encoding algorithms is proposed based on the proposed learning strategies. Extensive experimental results show that the proposed methods can reduce up to 24% of encoding time, with a negligible loss of coding efficiency. Moreover, it is demonstrated how a bimodal learning strategy can boost the performance of learning. Lastly, the proposed method has a very low computational overhead (0.2%), and uses existing components of a VVC encoder, which makes it much more practical compared to competing solutions.
HEVC (High Efficiency Video coding) as one of the newest international video coding standard, can achieve about 50% bit rate reduction compared with H.264/AVC (Advanced Video coding) with the same perceptual quality b...
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HEVC (High Efficiency Video coding) as one of the newest international video coding standard, can achieve about 50% bit rate reduction compared with H.264/AVC (Advanced Video coding) with the same perceptual quality by the use of flexible CTU (coding tree unit) structure, but at the same time, it also dramatically adds its computational complexity for HEVC. To reduce the computational complexity, a fast intra-prediction mode and CU (coding Unit) size decision algorithm based on prediction mode and coding bits grouping is presented for HEVC intra-encoding in this paper. The contribution of this paper lies in the fact that we successfully use the prediction mode grouping and coding bits grouping technologies to rapidly realize the optimal prediction mode and size decision for the current CU, thus saving much computation complexity for HEVC. Specifically, in our scheme, first, we use grouping technology to group 35 intra-prediction modes into 5 subsets of candidate modes list according to the texture complexity of current PU (Prediction Unit), and each subset only contains 11 intra-prediction modes, which can greatly reduce the traversing and calculating number of candidate mode in RMD (Rough Mode Decision);second, we use coding bits grouping technology to quickly judge whether the current CU needs to be further divided on the basis of the studying of texture complexity in the current CU, which can reduce many unnecessary prediction and partition operations for the current CU;at last we use the fast intra-mode prediction and CU size decision algorithm above to quickly realize the optimal encoding for the current CU in HEVC. As a result, the high computational complexity in HEVC intra-encoding can be efficiently reduced by our proposed scheme. And the simulation results of our experiments show that our proposed fast intra-coding algorithm based on prediction mode and coding bit grouping in this paper can reduce about 49.10% computational complexity on average only at a
In the recent Future Video coding (FVC) standard developed by the Joint Video Exploration Team (JVET), the quad-tree binary-tree (QTBT) block partition module makes use of rectangular block forms and additional square...
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In the recent Future Video coding (FVC) standard developed by the Joint Video Exploration Team (JVET), the quad-tree binary-tree (QTBT) block partition module makes use of rectangular block forms and additional square block sizes compared to quad-tree (QT) block partitioning module proposed in the predecessor High-Efficiency Video coding (HEVC) standard. This block flexibility, induced with the QTBT module, significantly improves compression performance while it dramatically increases coding complexity due to the brute force search for Rate Distortion Optimization (RDO). To cope with this issue, it is necessary to consider the unique characteristics of QTBT in FVC. In this paper, we propose a fast QT partitioning algorithm based on a deep convolutional neural network (CNN) model to predict coding unit (CU) partition instead of RDO which enhances considerably QTBT performance for intra-mode coding. Based on a suitable diversified CU partition patterns database, the optimization process is set up with three levels CNN structure developed to learn the split or non-split decision from the established database. Experimental results reveal that the proposed algorithm can accelerate the QTBT block partition structure by reducing the intra-mode encoding time by an average of 35% with a bit rate increase of 1.7%, allowing its application in practical scenarios.
The high-efficiency video coding (HEVC) standard uses 35 intra-prediction modes for 2(N) x 2(N) (N is an integer number ranging from six to two) luma blocks and five modes for chroma blocks. To find the luma block wit...
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The high-efficiency video coding (HEVC) standard uses 35 intra-prediction modes for 2(N) x 2(N) (N is an integer number ranging from six to two) luma blocks and five modes for chroma blocks. To find the luma block with the minimum rate-distortion, it must perform 11935 different rate-distortion cost calculations. Although this approach improves coding efficiency compared to the previous standards such as H.264/AVC, computational complexity is increased significantly. In this paper, an intra-prediction technique has been described to improve the performance of the HEVC standard by minimizing its computational complexity. The proposed algorithm consists of two stages. The first stage, called prediction unit size decision (PUSD) was introduced to decrease evaluation of prediction unit sizes by ~ 38%. The second stage called prediction mode fast decision (PMFD) was developed to minimize the number of modes in the rough mode decision (RMD) stage. The simulation results show that the time complexity is decreased by ~ 47%, while the BD rate is increased by 1.08%, and PSNR is decreased by 0.04 db. Accordingly, the proposed algorithms have a negligible effect on the video quality with great saving in the time complexity.
Versatile Video coding (VVC) promised to provide the same video quality as HEVC with 50 % bitrate reduction, which was introduced in 2020. Our suggested method for VVC intra-coding is residue super-resolution convolut...
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The high efficiency video coding (HEVC)-based 3D video coding (3D-HEVC) has been recently standardized by the Joint Collaborative Team on 3D video coding as a 3D extension of HEVC. The 3D-HEVC has been developed to im...
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The high efficiency video coding (HEVC)-based 3D video coding (3D-HEVC) has been recently standardized by the Joint Collaborative Team on 3D video coding as a 3D extension of HEVC. The 3D-HEVC has been developed to improve the coding efficiency of Multi-view Video plus Depth. intra-prediction is considered as an important technique in image and video compression, which aims to exploit spatial correlation within one picture. The use of the variable coding unit size and multiple intra-prediction modes makes the intra-coding of 3D-HEVC very efficient. However, the computational complexity is increased significantly. This paper presents a low complexity mode decision algorithm for 3D-HEVC intra-prediction based on local edge information.
Recently, High Efficiency Video coding (HEVC) standard has emerged to achieve high coding efficiency performance when introducing several specific modules. One of the most influential new tools is code the tree unit (...
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ISBN:
(纸本)9781728176680
Recently, High Efficiency Video coding (HEVC) standard has emerged to achieve high coding efficiency performance when introducing several specific modules. One of the most influential new tools is code the tree unit (CTU) partition structure. In the HEVC standard, a CTU becomes a block-shaped region replacing a macroblock based on H.264/AVC. This module achieves a significant gap of compression performance at the expense of encoding complexity increases under All-intra configuration. Block unit structure can be split into nested coding units (CUs) (range sizes 64x64 to 8x8) after applying the Rate-Distortion Optimization (RDO) process which becomes a bottleneck for feasible implementation. To resolve this issue, we suggest substituting the brute force RDO search with a LeNet-5 model for a CU partition at intra-mode. First, we created a database for the HEVC intra-mode in order to learn the learning model. Subsequently, a LeNet5 model is proposed for predicting the HEVC CU partition. Experimental results indicate that the proposed algorithm can speed up the CU partition structure by reducing the intra-mode encoding time by an average of 87.47% while degrading by 3.64% the compression performance. Our CU decision scheme outperforms all state-of-the-art solutions in terms of complexity reductions.
As the newest video coding standard, High Efficiency Video coding (HEVC) greatly enhances the encoding performance of H. 264/AVC. However, HEVC also has high computational complexity, which limits application of this ...
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As the newest video coding standard, High Efficiency Video coding (HEVC) greatly enhances the encoding performance of H. 264/AVC. However, HEVC also has high computational complexity, which limits application of this new standard. In this paper, we propose a fast DEA-based intra-coding algorithm, including block partitioning;prediction mode selection and edge offset (EO) class decision algorithms. The idea behind the proposed algorithm is to utilize the texture characteristics of the encoding image, which are quantified by dominant edge assent (DEA) and its distribution, to reduce the decision space. Specifically, for block partitioning, we propose the most possible depth range (MPDR) and employ DEA to determine whether the current coding block can use the MPDR to predict the partitioning depth or not;for intra-prediction mode selection, we use DEA and its distribution to reduce the range of prediction direction;for the EO class decision, we use DEA to determine the EO class of the sample adaptive offset. We integrate the proposed algorithm into the test model HM 13.0 and present a detailed comparative analysis. Experimental results show that the proposed fast DEA-based intra-coding algorithm reduces the computational complexity of HM 13.0 to about 46% in encoding time with 2.08% increases in the Biontegaard-Delta bitrate (BD-rate). Moreover, the proposed algorithm also demonstrates better performance over other state-of-the-art work.
This paper proposes a downsampling information-based intra-coding scheme which consists of two parts, preprocessing stage and fast intra-coding stage. Three downsampling information-based fast decision algorithms are ...
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This paper proposes a downsampling information-based intra-coding scheme which consists of two parts, preprocessing stage and fast intra-coding stage. Three downsampling information-based fast decision algorithms are proposed in fast intra-coding stage. Moreover, a parallelized architecture for the proposed fast intra-coding scheme is proposed. The preprocessed downsampling stage can be executed with intra-coding stage in parallel. The proposed architecture fully makes use of this feature to improve the throughput and fragment data dependency. Experimental results demonstrate that the proposed algorithms achieves on average 60.4% reduction in encoding time with negligible coding efficiency loss, compared with original HEVC.
This paper proposes a rate control (RC) algorithm for intra-coded sequences (I-frames) within the context of block-based predictive transform coding (PTC) that employs piecewise linear approximations of the rate-disto...
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ISBN:
(纸本)9781538646588
This paper proposes a rate control (RC) algorithm for intra-coded sequences (I-frames) within the context of block-based predictive transform coding (PTC) that employs piecewise linear approximations of the rate-distortion (RD) curve of each frame. Specifically, it employs information about the rate (R) and distortion (D) of already compressed blocks within the current frame to linearly approximate the slope of the corresponding RD curve. The proposed algorithm is implemented in the High-Efficiency Video coding (HEVC) standard and compared with the current HEVC RC algorithm, which is based on a trained rate-lambda (R-lambda) model. Evaluations on a variety of intra-coded sequences show that the proposed RC algorithm not only attains the overall target bit rate more accurately than the current RC algorithm but is also capable of encoding each I-frame at a more constant bit rate according to the overall bit budget, thus avoiding high bit rate fluctuations across the sequence.
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