一种基于时域相关性的高性能视频编码快速帧间预测单元模式判决算法

为了降低高性能视频编码(HEVC)的编码计算复杂度,根据视频时域上高度相关性的特点,该文提出一种快速高性能视频编码(HEVC)帧间预测单元(PU)模式判决算法。分析了时域上相邻帧两帧相同位置编码单元(CU)的PU模式之间的相关性;同时,针对视频中可能存在对象运动,还分析了前一帧对应位置CU的周边CU与当前帧中当前CU间PU模式的相关性。根据分析的时域相关性,跳过当前CU中冗余的PU模式,从而降低编码复杂度。实验结果表明,在编码效率和峰值信噪比(PSNR)损失很小的情况下,在目前已有的HEVC快速帧间预测算法的基础上,进一步降低了31.30%的编码时间。     

基于时空相关性的HEVC帧间模式决策快速算法

新一代的高效率视频编码标准HEVC采用编码树单元（CTU）四叉树划分技术和多达10种的帧间预测单元（PU）模式,有效地提高了编码压缩效率,但也极大地增加了编码计算复杂度。为了减少编码单元（CU）的划分次数和候选帧间PU模式个数,提出了一种基于时空相关性的帧间模式决策快速算法。首先,利用当前CTU与参考帧中相同位置CTU、当前帧中相邻CTU的深度信息时空相关性,有效预测当前CTU的深度范围。然后,通过分析当前CU与其父CU之间的最佳PU模式空间相关性,以及利用当前CU已估计PU模式的率失真代价,跳过当前CU的冗余帧间PU模式。实验结果表明,提出的算法与HEVC测试模型（HM）相比,在不同编码配置下降低了52%左右的编码时间,同时保持了良好的编码率失真性能;与打开快速算法选项的HM相比,所提算法进一步降低了30%左右的编码时间。     

基于零块分析的高效视频编码通信

为了实现高清、超高清视频实时编码通信传输, 针对高效视频编码(HEVC)帧间编码计算复杂度过高的问题,根据图像的文理复杂度和 编码单元的零块统计特征,提出一种新的HEVC快速帧间模式判决算法。根据Merge模式下 整单元一分为四的4个子编码单元纹 理相似度确定是否提前终止编码单元(CU)划分,同时利用帧间2N×2N预测模式下零系数与非零系数分布的区域统计特征,选择符合零块分 布特征的最佳预测单元(PU)模式。实验结果表明,在低延迟B(LDB,low-delay B)和随机访 问(RA,random access)配置条件下,提出的算法在保持编码 性能基本不变的情况下,HEVC帧间预测编码时间分别平均减少了60.2%与59.4%。     

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。     

基于多分支网络的深度图帧内编码单元快速划分算法

3维高效视频编码(3D-HEVC)标准是最新的3维(3D)视频编码标准,但由于其引入深度图编码技术导致编码复杂度大幅增加。其中,深度图帧内编码单元(CU)的四叉树划分占3D-HEVC编码复杂度的90%以上。对此,在3D-HEVC深度图帧内编码模式下,针对CU四叉树划分复杂度高的问题,该文提出一种基于深度学习的CU划分结构快速预测方案。首先,构建学习深度图CU划分结构信息的数据集;其次,搭建预测CU划分结构的多分支卷积神经网络(MB-CNN)模型,并利用构建的数据集训练MB-CNN模型;最后,将MB-CNN模型嵌入3D-HEVC的测试平台,通过直接预测深度图帧内编码模式下CU的划分结构来降低CU划分复杂度。与标准算法相比,编码复杂度平均降低了37.4%。实验结果表明,在不影响合成视点质量的前提下,该文所提算法有效地降低了3D-HEVC的编码复杂度。     

基于时域相关性的快速HEVC帧间模式判决方法

高效率视频编码(HEVC)相比目前的国际视频编码标准H.264/AVC,在压缩率方面有了较大的提高,但也带来了巨大的编码计算复杂度。为了降低HEVC的编码计算复杂度,提出了一种快速HEVC帧间模式判决方法。统计并分析了相邻两帧之间对应块的编码单元(CU)和预测单元(PU)的时域相关性,在此相关性基础上,跳过了一些冗余CU分割层和PU预测模式,CU层最多只取两种PU预测模式,从而大大减少了所需要进行的率失真代价计算的数量,实验结果显示,与HM7.0相比,该方法在仅损失了0.21%-1.66%的压缩率和0.01~0.09 dB的峰值信噪比的前提下,降低了52.3%~63.5%的编码时间。     

基于多时-空相关的AVS2快速帧间预测算法

针对新一代视频标准AVS2引进四叉树分割、多参考帧等技术而带来的帧间预测复杂度增加的问题,提出一种基于多时-空相关的快速帧间预测算法.该算法利用上下层相邻编码单元(Coding Unit,CU)和空时域相邻CU在预测模式选择上的相关性,计算当前CU的模式复杂度,根据复杂度为当前CU分配合适的候选预测模式;同时利用相邻预测单元(Prediction Unit,PU)在参考帧选择上的相关性,计算当前PU的参考帧索引,根据索引为当前PU分配合适的候选参考帧.实验表明,该算法在BD-Rate(Bjontegaard delta bit rate)增加1.22％,BD-PSNR(Bjontegaard delta peak signal-to-noise rate)降低0.04 dB的前提下,平均减少47.54％的编码时间.     

基于运动特征的HEVC帧间模式快速判决算法*

为降低HEVC帧间预测编码过程的计算复杂度,提出了一种新的基于运动特征的编码单元(Coding Units,CU)及预测单元(Prediction Units,PU)快速判决算法.首先,通过二阶帧差法检测运动剧烈程度,并分析运动特征对CU及PU时域相关性的影响.然后,利用前帧相同位置CU及PU的最优模式预测当前CU的深度范围,并根据运动剧烈程度优化预测范围.最后,根据预测范围跳过和终止部分不必要的CU深度和PU模式,从而加快帧间编码速度.在HM12.0平台上的实验测试表明,该算法在BDPSNR仅降低0.068 dB、BDBR只增加1.530％的情况下,提高了48.389％的编码速度.     

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。     

一种屏幕内容编码帧间模式快速选择算法

屏幕内容编码(SCC)作为高效视频编码(HEVC )的扩展,在压缩屏幕内容方面有着显著的效果, 但也导致了编码器计算复杂度较高的问题。为此,本文提出一种屏幕内容编码帧间模式快速 选择算法。首 先,根据像素点亮度值的变化情况,提前判断出静止区域并使用Skip模式;其次,根据屏幕 内容多包含有 水平及竖直边缘的特点,利用编码单元(CU)的水平及竖直活动性确定相应的预测单元(PU )划分模式, 减少帧间预测时需要遍历的PU个数;最后,根据时空域相邻CU的深度信息预测当前CU的深度 范围,跳 过不必要的深度遍历。实验结果表明,与SCM-8.0相比,在随机接入与低延时两种编码模式 下,本文所提 算法分别节省43.6%和49.09%的编码时间,码 率分别上升3.06%和3.43%,视频质量几乎不变 。     

Efficient prediction of CU depth and PU mode for fast HEVC encoding using statistical analysis

High Efficiency Video Coding (HEVC) adopts complex quadtree-structured CU (coding unit) and PU (prediction unit) modes. Thus, the search process for optimal modes causes high computational complexity in HEVC. To solve this problem, a fast coding algorithm is proposed. Thirteen neighboring CTUs (Coding Tree Unit) are divided into three classes with the k-means method, and are selectively used as the reference CTUs to calculate the predicted CU depth levels for the current CTU. Additionally, for every CU depth, we skip some rarely used PU modes to reduce the complexity of the PU selection algorithm. Experimental results show that compared with the HEVC reference software, our algorithm can achieve 56.71% time saving with Low Delay Configuration profile and 59.76% with Random Access Configuration profile, whereas values of BDBR (Bjontegaard Delta Bit Rate) are only 1.0517% and 0.9918%, respectively. Compared with five state-of-the-art algorithms, the proposed algorithm has significant encoding time reduction with good bitrate performances.     

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.     

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.     

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 prediction unit selection method for HEVC intra prediction based on salient regions

In order to reduce the computational complexity of the high efficiency video coding(HEVC) standard, a new algorithm for HEVC intra prediction, namely, fast prediction unit(PU) size selection method for HEVC based on salient regions is proposed in this paper. We first build a saliency map for each largest coding unit(LCU) to reduce its texture complexity. Secondly, the optimal PU size is determined via a scheme that implements an information entropy comparison among sub-blocks of saliency maps. Finally, we apply the partitioning result of saliency map on the original LCUs, obtaining the optimal partitioning result. Our algorithm can determine the PU size in advance to the angular prediction in intra coding, reducing computational complexity of HEVC. The experimental results show that our algorithm achieves a 37.9% reduction in encoding time, while producing a negligible loss in Bjontegaard delta bit rate(BDBR) of 0.62%.     

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。     

Fast CU size and prediction mode decision method for HEVC encoder based on spatial features

In the high-efficiency video coding (HEVC) standard, intra prediction has higher computational complexity compared with H.264/AVC (advanced video coding) because of increasing the number of intra prediction modes and also higher number of coding unit (CU) sizes. The HEVC encoder evaluates 35 prediction modes on five possible prediction unit (PU) sizes to find the one with the minimum rate–distortion cost. Although this approach improves coding efficiency, it is very time-consuming. In this paper, we propose a fast intra prediction method to reduce the complexity of I-frame coding. The proposed method consists of three stages which is based on smoothness spatial feature. In the first stage, a measure is introduced to estimate CU smoothness by using sum of absolute differences (SAD) among CU pixels in four directions. By considering that a smooth region can be predicted with larger CUs, when the measured smoothness parameter is lower than a predefined threshold, only the prediction modes in the current CU are evaluated. In the second stage, the number of intra prediction modes is reduced based on the calculated SADs in the previous stage. In the last stage, if the first three candidate modes resulted from rough mode decision stage in the previous PU and the current PU are similar, then the best mode prediction of the previous PU is selected as the best candidate mode. Experimental results indicate that the proposed method can reduce the coding time on average to 43 % and maintain coding video quality, whereas bitrate increases negligibly (0.5 %).     

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。     

SVM based approach for complexity control of HEVC intra coding

The High Efficiency Video Coding (HEVC) is adopted by various video applications in recent years. Because of its high computational demand, controlling the complexity of HEVC is of paramount importance to appeal to the varying requirements in many applications, including power-constrained video coding, video streaming, and cloud gaming. Most of the existing complexity control methods are only capable of considering a subset of the decision space, which leads to low coding efficiency. While the efficiency of machine learning methods such as Support Vector Machines (SVM) can be employed for higher precision decision making, the current SVM-based techniques for HEVC provide a fixed decision boundary which results in different coding complexities for different video content. Although this might be suitable for complexity reduction, it is not acceptable for complexity control. This paper proposes an adjustable classification approach for Coding Unit (CU) partitioning, which addresses the mentioned problems of complexity control. Firstly, a novel set of features for fast CU partitioning is designed using image processing techniques. Then, a flexible classification method based on SVM is proposed to model the CU partitioning problem. This approach allows adjusting the performance-complexity trade-off, even after the training phase. Using this model, and a novel adaptive thresholding technique, an algorithm is presented to deliver video encoding within the target coding complexity, while maximizing the coding efficiency. Experimental results justify the superiority of this method over the state-of-the-art methods, with target complexities ranging from 20% to 100%.