Early Merge mode decision algorithm for 3D-HEVC based on learning model

3D-high efficiency video coding (3D-HEVC) standard is an extension of HEVC.Though 3D-HEVC effectively improves the compression efficiency of 3D video,it also brings huge computational complexity.To greatly reduce the 3D-HEVC coding complexity,an early Merge mode decision approach was proposed.The residual signal that encoded by the Merge mode was firstly extracted as feature information.A learning model was established in terms of the residual signals of the coding unit (CU) in current frame that used early Merge mode as the optimal mode.Finally,the residual signal was extracted for the Merge mode of current CU,and the learning model was used to predict whether the Merge mode was the optimal mode or not.Experimental results show that the proposed early Merge mode decision approach reduces the coding times of 3D-HEVC texture views and depth maps about 41.9% and 24.3%,respectively,and the coding performance degradation is almost negligible.Compared with existing early Merge mode decision approaches,the proposed approach further reduces the coding time,and can be easily integrated into the 3D-HEVC test model due to its design simplicity.     

Fast intra mode decision for depth coding in 3D-HEVC

The emergent 3D High Efficiency Video Coding (3D-HEVC) is an extension of the High Efficiency Video Coding (HEVC) standard for the compression of the multi-view texture videos plus depth maps format. Since depth maps have different statistical properties compared to texture video, various new intra tools have been added to 3D-HEVC depth coding. In current 3D-HEVC, new intra tools are utilized together with the conventional HEVC intra prediction modes for depth coding. This technique achieves the highest possible coding efficiency, but leads to an extremely high computational complexity which limits 3D-HEVC from practical applications. In this paper, we propose a fast intra mode decision algorithm for depth coding in 3D-HEVC. The basic idea of the proposed algorithm is to utilize the depth map characteristics to predict the current depth prediction mode and skip some specific depth intra modes rarely used in 3D-HEVC depth coding. Based on this analysis, two fast intra mode decision strategies are proposed including reduction of the number of conventional intra prediction modes, and simplification of depth modeling modes (DMMs). Experimental results demonstrate that the proposed algorithm can save 30 % coding runtime on average while maintaining almost the same rate-distortion (RD) performance as the original 3D-HEVC encoder.     

Multi-class support vector machine-based fast algorithm for 3D-HEVC depth video intra coding

The recursive splitting process of largest coding unit (LCU) and the mode search process of coding unit imposed enormous computational complexity on encoder. A multi-class support vector machine-based (MSVM) fast coding unit (CU) size decision algorithm for 3D-HEVC depth video intra-coding was proposed. The algorithm included two steps: off-line training and fast CU size and mode decision. In the process of off-line training, a MSVM model was constructed, where the texture complexity of current LCU, the optimal partition depth of its spatial neighboring LCU and co-located LCU in texture video were treated as feature vectors, and the optimal partition depth of LCU was utilized as corresponding class label. In the process of fast CU size and mode decision, features of LCU were extracted before cod-ing a LCU, then, a MSVM model was used to predict the class label. Finally, the class label that represents the largest parti-tion depth of the current LCU was employed to terminate the CU recursive splitting process and CU mode search process. Experimental results show that the proposed algorithm saves the encoding time of 3D-HEVC by 35.91% on average, and the encoding time of depth video by 40.04% on average, with negligible rendered virtual view image degradation.     

利用纹理结构的HEVC快速帧内模式选择算法

高性能视频编码（HEVC）是刚确立的最新一代的视频编码标准。对于帧内编码,HEVC最大的特点是采用了从64×64至8×8的编码单元划分和35种帧内预测模式,HEVC通过遍历所有的分块和帧内预测模式,选取最优的预测方式,这种帧内预测方式在提高预测精度的同时也大大增加了编码的计算复杂度。为了降低帧内预测的计算复杂度,HM2.0版以上的HEVC测试软件采用一种基于两步预测的快速帧内模式选择算法。在此基础上本文首先提出了一种基于纹理方向的快速粗选方案,减少参与计算的粗选模式数,进而提出基于结构相关的决策方法,利用同质图像区域的纹理相似性,减少参与率失真代价函数计算的候选模式数量,进一步降低了帧内预测计算复杂度。实验结果表明,本文所提出的快速算法,在保持编码质量基本不变的条件下,可以使基于两步预测的快速帧内模式选择时间缩短25%。      

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.     

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。     

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。     

纹理-深度图共同优化的3D-HEVC帧内快速算法

随着立体及3D视频需求的日益增多,针对3D视频编码方法的研究受到越来越多的关注。3D-HEVC编码标准对采用纹理和深度图格式融合的3D视频进行编码,由于加入了深度图编码,因此新增了深度图编码模式、组件间预测和分段直流编码等技术,使其编码复杂度急剧升高。为了减少3D-HEVC的编码时间,本文提出了针对纹理图和深度图的编码单元（Coding Unit,CU）尺寸提前决策快速算法。利用梯度矩阵和作为当前CU和子CU复杂度的判断依据,将CU分为三类：不划分CU(Non-Split Coding Unit,NSCU)、直接划分CU(Split Coding Unit,SCU)以及普通CU。对NSCU,跳过小尺寸的帧内预测过程;对SCU,直接跳过当前CU的帧内预测过程;对普通CU,执行原平台操作。实验结果表明,与原始平台相比,本文算法在合成视点质量基本不变的情况下,平均减少40.92%的编码时间;与最新的联合纹理-深度图优化的3D-HEVC快速算法相比,可以在质量相当的情况下减少更多的编码时间。     

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.     

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.     

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.     

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。      

Rough mode cost–based fast intra coding for high-efficiency video coding

The quadtree-based coding unit (CU) and transform unit (TU) structure, as well as various prediction units (PUs) of HEVC, considerably increase encoding complexity in intra coding and inter coding. This paper proposes a rough mode cost (RMC)-based algorithm for accelerating CU/TU depth decisions and PU mode decisions in HEVC intra coding. For CU depth decisions, RMC values are used for the fast determination of CU partition. In the case of PU mode decisions, modes with higher RMCs are removed from the candidate list to reduce the number of test modes. For TU depth decisions, the TU partition of the mode with the least RMC is used to determine the TU partitions of remaining modes. The proposed TU partitioning method demonstrates superior performance to the default method in reference software. The proposed algorithm can reduce encoding time by approximately 51% on average, with a 0.69% increase in the Bjøntegaard-Delta (BD) rate.     

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%。     

基于纹理方向和空域相关的HEVC帧内快速编码算法

为了降低高效视频编码(HEVC)的复杂度,提出了一种基于纹理方向和空域相关性的帧内快速编码算法。一方面,利用相邻编码单元(CU,coding unit)的最佳分割尺寸确定相关性的权重,并根据已编码CU的率失真代价值定义了CU分割尺寸的相关性因子,通过比较该因子提前终止CU分割;另一方面,利用Sobel算子求取预测单元(PU,prediction unit)子块的纹理方向,通过判定其纹理方向显著性确定相应的候选模式集,然后根据PU大小对所得的预测模式修正处理,最后遍历这些候选模式选取最优模式。实验结果表明:本文算法相对于原始HEVC编码方法,在全I帧模式下编码时间平均减少36.84%,BDBR(Bjntegaard delta bit rate)上升约0.81%,BDPSNR(Bjntegaard delta peak signal-to-noise rate)降低了0.047dB。     

x265的低复杂度帧内编码快速算法

为了降低x265的帧内编码复杂度,减少编码所需时间,本文针对新一 代高效视频编码标准(HEVC)帧内编码单元(CU)划分以 及预测单元(PU)模式选择的快速算法进行研究。采用经典的差分矩阵计算纹理复杂度,提出 了一种根据当前编码单元子块的纹理复杂 相似度与其编码所需要的总比特数以及量化参数之间的关系作为提前终止CU划分的判决 条件,通过增加SATD的计算进一 步减少PU模式选择的RDO候选列表的个数从而减少率失真代价计算的快速算法。实验结果 表明,与x265的标准算法相比, 该算法在平均峰值信噪比(PSNR)仅减少0.028dB和码率平均增加1.27%的情况下,能够平均减少32.34%的帧内编 码时间。     

Unified depth intra coding for 3D video extension of HEVC

With the development of high-efficiency video coding (HEVC), the newest video coding standard, 3D video extension of HEVC (3D-HEVC) has been actively investigated. Since 3D-HEVC uses multi-view texture and depth data for input, various coding tools have been added to HEVC. In 3D-HEVC, on top of the existing 35 HEVC intra modes, eight additional modes exist, which are specifically for depth coding. In this paper, we propose a unified depth intra coding method that incorporates such depth intra modes into the regular intra mode set. In particular, the most rarely used HEVC intra modes are replaced by depth intra modes. As a result, binarization for depth intra modes is removed. Furthermore, the most probable mode selection procedure is modified to consider the elimination of several angular intra modes. The proposed method is implemented and tested on 3D video HEVC test model version 7.0. Simulation results report 2.2 % synthesis gain under all-intra configuration.     

基于纹理平滑度的视点合成失真优化快速算法

针对3D-HEVC中深度图编码采用的视点合成失真优化方法的高复杂度问题,提出一种基于纹理平滑度的快速算法。首先结合帧内DC预测特性和统计学方法分析平坦纹理图中像素规律并设定基于纹理图平坦度的跳过准则;然后在深度图编码采用视点合成失真优化方法时提前分离出纹理图平坦区域所对应的深度图区域,并终止该区域像素基于虚拟视点合成的视点合成失真计算过程。实验结果证明该算法的有效性,能在保持编码质量的同时减少大量编码时间。     

Fast encoder decision for texture coding in 3D-HEVC

As a 3D extension of the High Efficiency Video Coding (HEVC) standard, 3D-HEVC is developed to improve the coding efficiency of multi-view video. However, the improvement of the coding efficiency is obtained at the expense of a computational complexity increase. How to relieve the computational burden of the encoder is becoming a critical problem in applications. In this paper, a fast encoder decision algorithm to encode the dependent texture views is proposed, where two strategies to accelerate encoder decision by exploiting inter-view correlations are utilized. The first one is an early merge mode decision algorithm, and the second one is an early CU splitting termination algorithm. Experimental results show that the proposed algorithm can achieve 47.1% encoding time saving with overall 0.1% BD-rate reduction compared to HTM (3D-HEVC test model) version 7 under the common test condition (CTC). Both of the two strategies have been adopted into the 3D-HEVC reference software and enabled as a default encoding process under CTC.     

3D-HEVC深度图像帧内编码单元划分快速算法

针对3维高性能视频编码(3D-HEVC)中深度图像帧内编码单元(Coding Unit, CU)划分复杂度高的问题,该文提出一种基于角点和彩色图像的自适应快速CU划分算法。首先利用角点算子,并根据量化参数选取一定数目的角点,以此进行CU的预划分;然后联合彩色图像的CU划分对预划分的CU深度级进行调整;最后依据调整后的CU深度级,缩小当前CU的深度级范围。实验结果表明,与原始3D-HEVC的算法相比,该文所提算法平均减少了约63%的编码时间;与只基于彩色图像的算法相比,该文的算法减少了约13%的编码时间,同时降低了约3%的平均比特率,有效地提高了编码效率。