The scalable extension of the high efficiency video coding standard (SHVC) combines the large compression efficiency and high visual quality of HEVC, with the possibility of encoding different versions of the same vid...
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The scalable extension of the high efficiency video coding standard (SHVC) combines the large compression efficiency and high visual quality of HEVC, with the possibility of encoding different versions of the same video in a single bitstream. However, this comes at the cost of high computational complexity. In this context, many research works aim to reduce this complexity for texture images. We aim at achieving the same objective, but for depth maps whose characteristics make them different from conventional texture images. Depth maps are indeed characterized by areas of smoothly varying grey levels separated by sharp discontinuities at object boundaries. Preserving these discontinuities is crucial to enable high quality of synthesized views at the receiver side. In this paper, we propose a fast depth maps encoding scheme for quality scalable HEVC while exploiting depth maps characteristics, SHVC codingunit (CU) depth information and the correlation between the Base Layer (BL) and the Enhancement Layers (ELs) of SHVC. If a CU corresponds to a depth smooth region, we maintain the same best coding depth of its co-located in the BL. If a CU is a sharp region, the best coding depth is computed in the same way as in the original SHVC. Experiments are conducted and satisfying results are obtained as the proposed method improves the SHVC coding speed without a significant impact on the synthesized views Rate-Distortion tradeoff
In almost all video applications, a video rate control algorithm (RCA) is used by the encoder. The RCA tunes the quantization parameter (QP) to match the encoded bit rate to the available capacity of the communication...
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In almost all video applications, a video rate control algorithm (RCA) is used by the encoder. The RCA tunes the quantization parameter (QP) to match the encoded bit rate to the available capacity of the communication channel or storage media. Conventional RCAs usually utilize a rate-quantization (R-Q) or a rate-distortion (R-D) model for rate control. A content-based R-Q model for intra coding tree units (CTUs) of the high-efficiency video coding standard is proposed. The model is a convolutional neural network that observes pixels of a CTU and its intraprediction reference pixels and it estimates required bit counts for intracoding the CTU for all QP values simultaneously. The proposed model can be easily used by any video RCA. A given RCA just selects a proper QP for which the estimated bit counts are closer to the allocated bit budget. The evaluation results show a high accuracy for the model. According to simulation results, the mean absolute normalized bit error at CTU level is 19.66% and it decreases to 6.85% at the frame level. Compared with similar networks, the proposed structure has a very low computational complexity. (C) 2022 SPIE and IS&T
High Efficiency Video coding (HEVC) standard employs coding tree unit (CTU) quadtree partition technology to improve the compression efficiency significantly, but it also consumes an enormous encoding computational co...
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ISBN:
(纸本)9781467390989
High Efficiency Video coding (HEVC) standard employs coding tree unit (CTU) quadtree partition technology to improve the compression efficiency significantly, but it also consumes an enormous encoding computational complexity. In this paper, a fast CTU depth prediction algorithm is proposed. First, the spatio-temporal correlation of CTU depths among the current CTU, the spatial neighbor CTUs, and the same located CTU in the reference frame are utilized to predict the depth range of current CTU. Second, the rate-distortion (RD) costs of the spatio-temporal neighbor codingunits (CUs) and the parent CU are employed to further reduce CU partitions. Experiment results show that the proposed algorithm reduces the encoding time effectively with maintaining a good RD performance as the HEVC test Model (HM). Compared to the state-of-the-art depth prediction algorithm, the proposed algorithm also obtains about 10% more time saving for various test sequences, while has a similar RD performance.
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 ...
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ISBN:
(纸本)9781479957521
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 codingunits 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 CTU quadtree depth decision algorithm to reduce the computational complexity of HEVC. Firstly, based on the best CTU 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.
Standardized in 2014, multiview extension of high efficiency video coding (MV-HEVC) offers significantly better compression performance of up to 50% for multiview and 3D videos compared to multiple independent single ...
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Standardized in 2014, multiview extension of high efficiency video coding (MV-HEVC) offers significantly better compression performance of up to 50% for multiview and 3D videos compared to multiple independent single view HEVC coding. However, the extreme high computational complexity of MV-HEVC demands significant optimization of the encoder. In this work, we propose a series of optimization techniques at various levels of abstraction: non-aggregation massively parallel motion estimation (ME) and disparity estimation (DE) for prediction units, fractional and bidirectional ME/DE, quantization parameter-based early termination of coding tree unit (CTU), and optimized resource-scheduled wave front parallel processing for CTU. When evaluated over three views for all available official multiview video coding test sequences, proposed optimization outperforms the anchor encoder by average factor of 5.4 at the cost of 4.4% bitrate (DBR) increase at no loss in PSNR, or alternatively a PSNR degradation of 0.12 dB at no change to the DBR.
In this paper, we propose a Reinforcement Learning (RL) based codingunit (CU) early termination algorithm for High Efficiency Video coding (HEVC). RL is utilized to learn a CU early termination classifier independent...
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In this paper, we propose a Reinforcement Learning (RL) based codingunit (CU) early termination algorithm for High Efficiency Video coding (HEVC). RL is utilized to learn a CU early termination classifier independent of depths for low complexity video coding. Firstly, we model the process of CU decision as a Markov Decision Process (MDP) according to the Markov property of CU decision. Secondly, based on the MDP, a CU early termination classifier independent of depths is learned from trajectories of CU decision across different depths with the end-to-end actor-critic RL algorithm. Finally, a CU decision early termination algorithm is introduced with the learned classifier, so as to reduce computational complexity of CU decision. We implement the proposed scheme with different neural network structures. Two different neural network structures are utilized in the implementation of RL based video encoder, which are evaluated to reduce video coding complexity by 34.34% and 43.33%. With regard to Bjontegaard delta peak signal-to-noise ratio and Bjontegaard delta bit rate, the results are -0.033 dB and 0.85%, -0.099 dB and 2.56% respectively on average under low delay B main configuration, when compared with the HEVC test model version 16.5. (C) 2019 Elsevier Inc. All rights reserved.
This paper designs a novel method to reduce the coding complexity of 3D-HEVC encoder by utilizing the properties of human visual perception. Two vision-oriented edge detections are proposed: for colour texture detecti...
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This paper designs a novel method to reduce the coding complexity of 3D-HEVC encoder by utilizing the properties of human visual perception. Two vision-oriented edge detections are proposed: for colour texture detection, the authors adopt the Just-Noticeable Distortion (JND);for depth map, the authors combine the Sample Adaptive Offset (SAO) and the Just Noticeable Depth Difference (JNDD) model. The authors also analyse the properties of colour texture and depth map to classify the coding tree unit (CTU) into various kinds of types, including complex-edge CTU, moderate-edge CTU and homogeneous CTU. Besides, fast mode decisions and early termination criteria are performed individually on each type of CTUs according to their characteristics. Especially for those CTUs with more edge information, the proposed projection-based fast mode decision and residual-based early termination preserve important colour texture while speeding up the coding at the same time. The proposed vision-oriented algorithm reduces 31.981% of the overall average coding time with only 1.580% BD-Bitrate increase. Experimental results show that the proposed algorithm can provide considerable time-saving while still maintain the video quality, which outperforms the previous researches.
In this paper, a fast inter mode decision algorithm, called the unimodal model-based inter mode decision (UMIMD), is proposed for the latest video coding standard, the high-efficiency video coding. Through extensive s...
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In this paper, a fast inter mode decision algorithm, called the unimodal model-based inter mode decision (UMIMD), is proposed for the latest video coding standard, the high-efficiency video coding. Through extensive simulations, it has been observed that a unimodal model (i.e., with only one global minimum value) can be established among the size of different prediction unit (PU) modes and their resulted rate-distortion (RD) costs for each quad-tree partitioned coding tree unit (CTU). To guarantee the unimodality and further search the optimal operating point over this function for each CTU, all the PU modes need to be first classified into 11 mode classes according to their sizes. These classes are then properly ordered and sequentially checked according to the class index, from small to large so that the optimal mode can be early identified by checking when the RD cost starts to arise. In addition, an effective instant SKIP mode termination scheme is developed by simply checking the SKIP mode against a pre-determined threshold to further reduce the computational complexity. The extensive simulation results have shown that the proposed UMIMD algorithm is able to individually achieve a significant reduction on computational complexity at the encoder by 61.9% and 64.2% on average while incurring only 1.7% and 2.1% increment on the total Bjontegaard delta bit rate (BDBR) for the low delay and random access test conditions, compared with the exhaustive mode decision in the HEVC. Moreover, the experimental results have further demonstrated that the proposed UMIMD algorithm outperforms multiple state-of-the-art methods.
A structural similarity (SSIM)-based game theory (GT) approach is proposed for rate-distortion (R-D) optimized CTU-level bit allocation in high efficiency video coding (HEVC). First, a SSIM-based bargaining game is fo...
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A structural similarity (SSIM)-based game theory (GT) approach is proposed for rate-distortion (R-D) optimized CTU-level bit allocation in high efficiency video coding (HEVC). First, a SSIM-based bargaining game is formulated and the Nash bargaining solution (NBS) is proposed, in which a SSIM-based initial minimum utility is defined. Second, we propose a two-stage remaining bit refinement-based bit allocation scheme. The optimization scheme of the SSIM-based bargaining game sufficiently considers the different R-D characteristics of coding tree units (CTUs), in which the feasible utility set is proved to be convex based on the proposed SSIM-based utility and R-SSIM model. Compared with the other state-of-the-art CTU-level bit allocation methods, the R-D performance improvements on Bjontegaard delta bit-rate (BD-BR), Bjontegaard delta peak-signal-to-noise-ratio (BD-PSNR), and BD-SSIM metrics of the proposed method can averagely achieve significant gains, respectively. The achieved R-D performance gains have been very close to the coding performance limits from the FixedQP method. Moreover, the proposed SSIM-GT method also maintains good performances on quality smoothness, bit rate accuracy, and encoding complexity.
To alleviate the computation burden of the depth intra coding in 3D-HEVC, a complexity reduction scheme based on texture feature and spatio-temporal correlation is proposed. Firstly, a maximum splitting depth layer de...
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To alleviate the computation burden of the depth intra coding in 3D-HEVC, a complexity reduction scheme based on texture feature and spatio-temporal correlation is proposed. Firstly, a maximum splitting depth layer decision algorithm is proposed to reduce unnecessary splitting depth layer of the coding tree unit utilising the information of the previous encoded I frame in the same view. Secondly, a new texture complexity model is built by pixel-based statistical method combined with edge detection. Based on the proposed model, the codingunit block is divided into the smooth block, texture or edge block. On the codingunit level, an early termination of codingunit splitting algorithm for smooth blocks is proposed to filter out unnecessary coding blocks. Thirdly, on the predicting unit level, a fast candidate mode decision algorithm considering predicting unit's types and spatial correlation is proposed to decide the candidate mode list directly. Experimental results describe that the proposed algorithm reduces 53.8% depth intra coding time on average, with 0.43% BD-rate loss on synthesised views.
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