一种HEVC帧内预测编码CU结构快速选择算法

为了提升高效视频编码(HEVC) 帧内预测编码部分的编码效率,提出了一种HEVC帧内预测编码编码单元(CU)结构快速选择算 法。算法通过 对CTU(coding tree unit)四叉树结构的遍历过程进行优化,设计了两种不同的最优CU结 构快速决策算 法,分别从最大划分深度和最小划分深度开始遍历,并在每一步遍历之前,判断是否提前终 止遍历操作。 同时,在对每个CTU进行求解时,依据其纹理复杂度和当前编码状态,从两种算法中选择出 最优快速决策 算法对其进行求解。在HM 15.0的基础上实现了提出的快速选择算法 。实验结果表明,本文算法能够在 保证编码性能的同时,降低31.14%的编码时间,提高了HEVC的编码效 率。     

CU encoding depth prediction,early CU splitting termination and fast mode decision for fast HEVC intra-coding

High Efficiency Video Coding (HEVC) is a new video coding standard achieving about a 50% bit rate reduction compared to the popular H.264/AVC High Profile with the same subjective reproduced video quality. Better coding efficiency is attained, however, at the cost of significantly increased encoding complexity. Therefore, fast encoding algorithms with little loss in coding efficiency is necessary for HEVC to be successfully adopted for real applications. In this paper we propose a fast encoding technique applicable to HEVC all intra encoding. The proposed fast encoding technique consists of coding unit (CU) search depth prediction, early CU splitting termination, and fast mode decision. In CU search depth prediction, the depth of encoded CU in the current coding tree unit (CTU) is limited to predicted range, which is mostly narrower than the full depth range. Early CU splitting skips mode search of its sub-CUs when rate distortion (RD) cost of current CU is below the estimated RD cost at the current CU depth. RD cost and encoded CU depth distribution of the collocated CTU of the previous frame are utilized both to predict the encoding CU depth search range and to estimate the RD cost for CU splitting termination. Fast mode decision reduces the number of candidate modes for full rate distortion optimized quantization on the basis of the low complexity costs computed in the preceding rough mode decision step. When all these three methods are applied, proposed fast HEVC intra encoding technique reduces the encoding time of the reference encoder by 57% on the average, with only 0.6% of coding efficiency loss in terms of Bjontegaard delta (BD) rate increase under the HEVC common test conditions.     

基于深层特征学习的高效率视频编码中帧内快速预测算法

高效视频编码(HEVC)标准相对于H.264/AVC标准提升了压缩效率,但由于引入的编码单元四叉树划分结构也使得编码复杂度大幅度提升。对此,该文提出一种针对HEVC帧内编码模式下编码单元(CU)划分表征矢量预测的多层特征传递卷积神经网络(MLFT-CNN),大幅度降低了视频编码复杂度。首先,提出融合CU划分结构信息的降分辨率特征提取模块;其次,改进通道注意力机制以提升特征的纹理表达性能;再次,设计特征传递机制,用高深度编码单元划分特征指导低深度编码单元的划分;最后建立分段特征表示的目标损失函数,训练端到端的CU划分表征矢量预测网络。实验结果表明,在不影响视频编码质量的前提下,该文所提算法有效地降低了HEVC的编码复杂度,与标准方法相比,编码复杂度平均下降了70.96%。     

基于朴素贝叶斯分类的DVC-HEVC快速转码

分布式视频编码(DVC)与传统视频编码之间的转码为移动终端设备之间的低功耗视频通信提供了一种有效的实现思路。以DVC与HEVC转码为研究对象,利用DVC解码端信息,针对高效视频编码(HEVC)中复杂度极高的编码单元(CU)划分过程进行复杂度优化研究。在DVC解码端提取与CU划分相关的纹理复杂度、运动矢量及预测残差3种特征信息;在HEVC编码端基于朴素贝叶斯原理建立CU快速划分模型,模型生成后便可以通过输入特征信息对当前CU划分进行快速决策,避免大量率失真(RD)代价计算过程。实验结果表明,本方案在编码比特率略有上升的情况下大幅缩短了HEVC编码时间,平均下降幅度达到58.26%,且几乎不影响视频质量。     

A Fast Intra‐Prediction Method in HEVC Using Rate‐Distortion Estimation Based on Hadamard Transform

A fast intra‐prediction method is proposed for High Efficiency Video Coding (HEVC) using a fast intra‐mode decision and fast coding unit (CU) size decision. HEVC supports very sophisticated intra modes and a recursive quadtree‐based CU structure. To provide a high coding efficiency, the mode and CU size are selected in a rate‐distortion optimized manner. This causes a high computational complexity in the encoder, and, for practical applications, the complexity should be significantly reduced. In this paper, among the many predefined modes, the intra‐prediction mode is chosen without rate‐distortion optimization processes, instead using the difference between the minimum and second minimum of the rate‐distortion cost estimation based on the Hadamard transform. The experiment results show that the proposed method achieves a 49.04% reduction in the intra‐prediction time and a 32.74% reduction in the total encoding time with a nearly similar coding performance to that of HEVC test model 2.1.     

Machine learning based fast H.264/AVC to HEVC transcoding exploiting block partition similarity

Video transcoding is to convert one compressed video stream to another. In this paper, a fast H.264/AVC to High Efficiency Video Coding (HEVC) transcoding method based on machine learning is proposed by considering the similarity between compressed streams, especially the block partition correlations, to reduce the computational complexity. This becomes possible by constructing three-level binary classifiers to predict quad-tree Coding Unit (CU) partition in HEVC. Then, we propose a feature selection algorithm to get representative features to improve predication accuracy of the classification. In addition, we propose an adaptive probability threshold determination scheme to achieve a good trade-off between low coding complexity and high compression efficiency during the CU depth prediction in HEVC. Extensive experimental results demonstrate the proposed transcoder achieves complexity reduction of 50.2% and 49.2% on average under lowdelay P main and random access configurations while the rate-distortion degradation is negligible. The proposed scheme is proved more effective as comparing with the state-of-the-art benchmarks.     

CTU depth decision algorithms for HEVC: A survey

High Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The Coding Tree Unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64 × 64 pixels. Each CTU is then divided recursively into a number of equally sized square areas, known as Coding Units (CUs). Although this diversity of frame partitioning increases encoding efficiency, it also causes an increase in the time complexity due to the increased number of ways to find the optimal partitioning. To address this complexity, numerous algorithms have been proposed to eliminate unnecessary searches during partitioning CTUs by exploiting the correlation in the video. In this paper, existing CTU depth decision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches. Statistics approaches are further subdivided into neighboring and inherent approaches. Neighboring approaches exploit the similarity between adjacent CTUs to limit the depth range of the current CTU, while inherent approaches use only the available information within the current CTU. Machine learning approaches try to extract and exploit similarities implicitly. Traditional methods like support vector machines or random forests use manually selected features, while recently proposed deep learning methods extract features during training. Finally, this paper discusses extending these methods to more recent video coding formats such as Versatile Video Coding (VVC) and AOMedia Video 1(AV1).     

高效率视频编码帧内预测编码单元划分快速算法

高效率视频编码(HEVC)采用基于编码单元(CU)的四叉树块分区结构,能灵活地适应各种图像的纹理特征,显著提高编码效率,但编码复杂度大大增加,该文提出一种缩小深度范围且提前终止CU划分的快速CU划分算法。首先,在学习帧中,基于Sobel边缘检测算子计算一帧中各深度边缘点阈值,缩小后面若干帧中CU遍历的深度范围;同时,统计该帧中各CU划分为各深度的率失真(RD)代价,计算各深度的RD代价阈值。然后,在后续视频帧中,利用RD代价阈值在缩小的深度范围内提前终止CU划分。为了符合视频内容的变化特性,统计参数是周期性更新的。经测试,在平均比特率增加仅1.2%时,算法时间平均减少约59%,有效提高了编码效率。     

基于灰度差值的HEVC快速帧间编码研究

针对新一代视频压缩编码标准HEVC计算复杂度较高的特点,利用视频序列间时域上的相关性,提出了一种基于灰度差值的编码单元快速划分策略.该策略根据当前编码块与参考块之间的灰度差值进行运动条件判决,在进行编码之前提前确定当前编码单元的编码深度信息,减少帧间预测编码的次数,从而有效地降低了编码端的计算复杂度.实验结果表明,该算法在编码效率和峰值信噪比(PSNR)损失都很小的情况下,和HM标准中的帧间预测算法相比,平均降低了50.18％的编码时间.     

Self-learning residual model for fast intra CU size decision in 3D-HEVC

As an extension of the High Efficiency Video Coding (HEVC) standard, 3D-HEVC requires to encode multiple texture views and depth maps, which inherits the same quad-tree coding structure as HEVC. Due to the distinct properties of texture views and depth maps, existing fast intra prediction approaches were presented for the coding of texture views and depth maps, respectively. To further reduce the coding complexity of 3D-HEVC, a self-learning residual model-based fast coding unit (CU) size decision approach is proposed for the intra coding of both texture views and depth maps. Residual signal, which is defined as the difference between the original luminance pixel and the optimal prediction luminance pixel, is firstly extracted from each CU. Since residue signal is strongly correlated with the optimal CU partition, it is used as the feature of each CU. Then, a self-learning residual model is established by intra feature learning, which iteratively learns the features of the previously encoded coding tree unit (CTU) generated by itself. Finally, a binary classifier is developed with the self-learning residual model to early terminate CU size decision of both texture views and depth maps. Experimental results show the proposed fast intra CU size decision approach achieves 33.3% and 49.3% encoding time reduction on average for texture views and depth maps with negligible loss of overall video quality, respectively.     

基于决策树的HEVC SCC帧内编码快速算法

为减少HEVC屏幕内容编码的编码时间,提高编码 效率,本文提出了一种基于决策树的HEVC屏幕内容帧内编码快速 CU划分和简单PU模式选择的算法。对视频序列特性分析,提取有效的特征值,生成决策树模 型。使用方差、梯度信息熵和 像素种类数用于生成CU划分决策树,使用平均非零梯度、像素信息熵等用于生成PU模式分类 决策树。在一定深度的决策 树模型中,通过对相应深度的CU的特征值的计算快速决策当前CU的划分与PU模式的类型。这 种利用决策树做判决的算法 通过减少CU深度和PU的模式遍历而降低编码复杂度,达到快速帧内编码的效果。实验结果表 明,与HEVC屏幕内容的标 准算法相比,该算法在峰值信噪比(PSNR)平均下降0. 05 dB和码率 平均增加1.15%的情况下,能平均减少30.81% 的编码时间。     

基于感兴趣区域的高性能视频编码帧内预测优化算法

针对高性能视频编码(HEVC)帧内预测编码算法复杂度较高的问题,该文提出一种基于感兴趣区域的高性能视频编码帧内预测优化算法。首先,根据图像显著性划分当前帧的感兴趣区域(ROI)和非感兴趣区域(NROI);然后,对ROI基于空域相关性采用提出的快速编码单元(CU)划分算法决定当前编码单元的最终划分深度,跳过不必要的CU划分过程;最后,基于ROI采用提出的预测单元(PU)模式快速选择算法计算当前PU的能量和方向,根据能量和方向确定当前PU的预测模式,减少率失真代价的相关计算,达到降低编码复杂度和节省编码时间的目的。实验结果表明,在峰值信噪比(PSNR)损失仅为0.0390 dB的情况下,所提算法可以平均降低47.37%的编码时间。     

Fast algorithm based on the sole- and multi-depth texture measurements for HEVC intra coding

In High Efficiency Video Coding (HEVC), intra coding plays an important role, but also involves huge computational complexity due to a flexible coding unit (CU) structure and a large number of prediction modes. This paper presents a fast algorithm based on the sole- and multi-depth texture measurements to reduce the complexity from CU size and prediction mode decisions. For the CU size decision, evaluation results in the CU coding with one and multiple depths are utilized to classify CUs into heterogeneous, homogeneous, depth-prominent and other ones. Fast CU size decisions are made for different kinds of CUs. For the prediction mode decision, the tendencies for different CU sizes are detected based on multiple depths. The number of searching modes is decreased adaptively for the CU size with fewer tendencies. Experimental results show the proposed algorithm by off-line training reduces 53.32% computational complexity, with 1.47% bit-rate increasing.     

Low-Complexity Intra-Coding Scheme for HEVC

Compared with H.264/AVC, the latest video standard High Efficiency Video Coding (HEVC) known as H.265 improves the coding efficiency by adopting the quadtree splitting structure which is flexible in representing various textural and structural information in images. However, the computational complexity is dramatically increased, especially in the intra-mode decision process owing to supporting more partitions and modes. In this paper, we propose a low-complexity algorithm for HEVC intra-coding, which consists of a fast coding unit (CU) size decision (FCUSD) method and a fast prediction unit (PU) mode decision (FPUMD) method. In FCUSD, unnecessary CU sizes are skipped early according to the depth level of neighboring CUs and the rate distortion (RD) cost threshold derived from the former coded frame. In FPUMD, the PU mode and RD cost correlations between different depth levels are utilized to terminate unnecessary candidate modes. Experimental results demonstrate that the proposed algorithm can achieve about 50.99 % computational complexity reduction on average with 1.18 % BD-rate increase and 0.08 dB BD-psnr loss.     

3D-HEVC合并模式快速判决方法研究

基于高效视频编码的3D视频编码（3D-HEVC）是目前正在研究的新一代3D视频编码标准。为降低3D-HEVC中模式选择的计算复杂度,根据非独立视点纹理图中合并模式采用率高的特点,该文提出了一种3D-HEVC合并模式快速判决方法。在B帧中,分析了当前编码单元（CU）与视点方向参考帧中参考块间编码模式的相关性;在P帧中,分析了位于相邻划分深度的CU间编码模式的相关性。根据分析的视点间和划分深度间的相关性设计快速判决条件,预判采用合并/合并-跳过模式编码的CU,判别出的CU在模式选择过程中只检查相关的候选预测模式,从而降低计算复杂度。实验结果表明,与3D-HEVC原始算法相比,该文算法能够在率失真性能损失很小的前提下,平均节省11.2%的总编码时间和25.4%的非独立视点纹理图的编码时间。      

Early merge mode decision for texture coding in 3D-HEVC

As the upcoming 3D video coding standard, high efficiency video coding (HEVC) based 3D video coding (3D-HEVC) has been drafted. In 3D-HEVC, the computational complexity of mode decision process is significantly high due to exhaustive modes’ checks for coding units (CU) derived from recursive quad-tree partitioning. In this paper, we propose an early merge mode decision method for complexity reduction of dependent texture views. First, inter-view correlation and hierarchical depth correlation of coding modes are separately analyzed for B frame and P frame. Then, conditions to early determine merge mode coded CUs are derived based on the correlations. All of the early determined CUs only check merge modes in the mode decision process, which brings considerable complexity reduction. Experimental results demonstrate that the proposed method can achieve average 20.4% of encoding time saving for dependent texture views with negligible rate distortion performance loss.     

Fast coding unit partitioning method based on edge detection for HEVC intra-coding

High efficiency video coding (HEVC) standard is the latest video coding standard generation. It employs powerful coding tools to obtain improved compression efficiency. To better exploit the redundancies, HEVC adopts a very flexible quad-tree coding structure, allowing the encoder to use a block partition that matches the image features. This exhaustive technique may achieve a higher coding efficiency; however, it induces a significant computational complexity in the encoding engine. This paper proposes a new texture parameter for classifying digital videos as a first contribution and then introduces an efficient coding unit (CU) partitioning algorithm based on the early defined texture parameter in order to speed up the encoding process. In fact, the proposed technique is based on edge detection by performing SOBEL filtering in order to decide the appropriate CU size. Compared to the original HEVC, the average execution time-saving is about 31 % while maintaining almost the same output video quality.     

一种基于时空相关性的编码单元深度快速分级判决算法

新一代视频编码标准HEVC采用编码树单元(CTU) 结构进行编码,通过遍历比较所有不同深度编码 单元(CU)的率失真代价值得到最佳编码单元,在显著提升编码效率的同时,计算复杂度增 加数倍。为此,本文提出了一种基于时空相关的编码单元深度快速分级判决算法,通过量化 分析时 空相邻CTU/CU之间相关性权重,利用已编码时空相邻CTU/CU的最佳深度预测当前CTU/CU可能 的深度范围(DR)和深度值, 跳过和提前终止不必要的深度计算。仿真结果表明,在不同的编码配置条件下,本文算法在 保证编码性能的同时,平均可节省40%以上的编码时间 ,极大地降低了计算复杂度。     

一种基于组合神经网络的HEVC帧间预测方法

高效视频编码(HEVC)作为最新视频编码标准,有着非常高的压缩效率,但是由于各种新技术的提出,其编码复杂度也大大提高。复杂度对视频编码有着重要意义,低复杂度编码的研究非常必要。利用神经网络进行HEVC的分区预测为低复杂度编码提供了有效的解决方案。文中提出了一种基于卷积神经网络(CNN)和长短期记忆网络(LSTM)的组合网络架构来对帧间分区进行预测的方法,利用自建数据库对网络进行训练;文中设计了一种预搜索模块来建立训练数据库,仿真结果表明,神经网络的精度可达87%,利用该网络架构进行帧间预测可以实现52%~71%的复杂度节省。     

Fast intra-encoding algorithm for High Efficiency Video Coding

The emerging High Efficiency Video Coding (HEVC) standard provides equivalent subjective quality with about 50% bit rate reduction compared to the H.264/AVC High profile. However, the improvement of coding efficiency is obtained at the expense of increased computational complexity. This paper presents a fast intra-encoding algorithm for HEVC, which is composed of the following four techniques. Firstly, an early termination technique for coding unit (CU) depth decision is proposed based on the depth of neighboring CUs and the comparison results of rate distortion (RD) costs between the parent CU and part of its child CUs. Secondly, the correlation of intra-prediction modes between neighboring PUs is exploited to accelerate the intra-prediction mode decision for HEVC intra-coding and the impact of the number of mode candidates after the rough mode decision (RMD) process in HM is studied in our work. Thirdly, the TU depth range is restricted based on the probability of each TU depth and one redundant process is removed in the TU depth selection process based on the analysis of the HEVC reference software. Finally, the probability of each case for the intra-transform skip mode is studied to accelerate the intra-transform skip mode decision. Experimental results show that the proposed algorithm can provide about 50% time savings with only 0.5% BD-rate loss on average when compared to HM 11.0 for the Main profile all-intra-configuration. Parts of these techniques have been adopted into the HEVC reference software.