一种用于多视视频加深度联合编码的错误隐藏算法

ATM(AVC-based test model)测试模型实现了多视视频加深度(MVD)格式的联合编码,使得数据的压缩效率更高。然而,较高的压缩效率使得码流对传输错误非常敏感,极易产生错误扩散现象。针对ATM测试模型的编码顺序,提出一种用于MVD联合编码的错误隐藏算法。算法充分利用视点内、视点间及纹理视频与深度视频间的相关性,针对每个视点的不同特征提出了适应其视频特性的不同隐藏算法。实验表明,本文提出的算法可以在不增加算法复杂度的情况下,有效提高视频的主客观质量。     

基于时域相关性的自适应采样值补偿算法*

虽然自适应采样值补偿(SAO)滤波器能有效提高视频的主客观质量,但是SAO编码参数存在大量冗余,导致其不能适用于低比特率环境.因此,提出了一种基于时域相关性的自适应采样值补偿算法.通过率失真代价来选择当前帧是否复用最近已编码帧的SAO编码参数,并为每一帧设置一个标志位来标识是否复用.实验结果表明,与HTM原始SAO滤波器算法相比,该算法在主观质量和复杂度基本不变的情况下,3个视频分量的BD-rate平均分别减少1.06％、1.10％和0.99％.因此,所提出的算法能很好地改善SAO的性能.     

多视点纹理加深度编码的联合码率控制方法

码率控制技术是多视点视频编码和传输中一个关键的问题。为了提高三维(3D)视频的整体显示质量,包括虚拟视点质量和编码视点质量,提出一种多视点纹理加深度编码的联合码率控制方法。该算法研究了纹理和深度的关系,采用基于模型方法确定最优的纹理和深度之间的码率比例。根据各个视点编码结果的统计规律,不同的视频序列采用不同的视点间比特分配比例。实验结果表明,与目前流行的多视点码率控制算法相比,该算法在计算复杂度基本保持不变的情况下,平均码率控制误差在0.6%以内,客观质量PSNR最高可提高0.65 dB。     

基于最大可容忍深度失真模型的低复杂度深度视频编码

在保证虚拟视点绘制质量的前提下,如何降低深 度视频的编码复杂度是一个亟需解决的问题。本文提出了一 种基于最大可容忍深度失真(MTDD,maximum tolerable d epth distortion)模型的低复杂度深度编码算法,将MTDD模型引 入 到率失真(RD)代价函数,通过对编码单元(CU)进行失真度量判决 ,如果对当前CU进 行编码不会导致可察觉的绘制失真,则失真度量只考虑深度编码失真,否则失真度量同时考 虑深度编码失真和视点 合成失真(VSD),从而降低编码复杂度。实验结果表明,所 提出的算法在不 降低虚拟视点绘制质量和不增加编码码率的情况下,能显著降低深度视频的编码复杂度。     

多视点编码中一种快速的模式选择方法

在多视点视频编码中,多尺寸的运动估计和视点估计被用来选择宏块的最优编码模式。这些技术大大提高了编码效率,但是却带来了庞大的计算复杂度,从而影响了多视点视频编码的广泛应用。提出了一种新的模式选择的快速算法,利用了相邻视点的模式相关性来减少执行不需要的模式。实验表明,所提出的快速模式选择算法在不降低编码效率的同时可以显著降低计算复杂度。     

采用背景提取和自适应滤波的视频降噪算法

针对监控视频图像背景固定的特点,提出一种有效去除高斯噪声和脉冲噪声的降噪算法.首先通过分析噪声设计一种提取视频序列背景图像的算法,然后对运动区域采用自适应像素域滤波算法来进行处理.该算法根据最小可觉差和视频图像特征自适应地选择谐波均值滤波、加权算术平均滤波、α-截尾均值滤波和中值滤波.为评估降噪算法性能,将降噪处理前后的视频序列分别进行MPEG-2编码,并改变目标码率对比视频质量.实验结果显示:降噪处理后的视频能够用更少的(约50%)比特数获得相同的主、客观视频质量;或者用相同的比特数获得更高的视频质量.     

分布式多视点视频编码中的立体边信息生成

新兴的分布式多视点视频编码通过在解码端使用立体边信息可充分挖掘传统单视点的时间相关性和多视点特有的视间相关性.研究了一种多视点运动预测的立体边信息生成算法,提出了近似视差估计和编码端掩模融合算法.实验证明,解码端采用近似的视差矢量,编码端传递某些先验信息,都能有效提高立体边信息精度,降低计算复杂度.     

基于局部亮度直方图的自适应视频帧类型决策算法

视频帧类型决策是影响视频编码效率的关键因素之一。为提升x265视频编码器的编码性能,该文提出基于局部亮度直方图的自适应视频帧类型决策算法。首先,在64×64大小的编码树单元(CTU)级别上统计各帧局部亮度直方图,用帧间局部亮度直方图差异表征帧间场景变换程度;其次,引入帧内编码帧(I帧)检测窗,在检测窗内通过比较帧间场景变换程度自适应确定I帧;最后,根据帧间场景变换程度与迷你图像组(MiniGOP)大小之间的相关性确定MiniGOP大小,从而自适应确定普通P和B帧(GPB帧)及双向预测编码帧(B帧)。实验结果表明,与x265标准中的相关算法相比,所提算法能够有效降低x265的编码复杂度,可在减少近5%编码时间的前提下,实现视频I帧、GPB帧和B帧的高效自适应决策。     

一种基于虚拟视点合成的快速深度图编码方法

为了降低深度图编码复杂度并保证虚拟视点主观质量基本不变的情况下,本文面向三维视频系统采用最大可容忍深度失真(Maximum Tolerated Depth Distortions,MTDD)模型,并设计提出一种基于虚拟视点合成的快速深度图编码方案,它包括1)基于MTDD的LCU四叉树提前决策、2)检测是否对绘制失真敏感的竖直边缘区域等2个算法。首先,根据MTDD的可容忍性不同,给出应用于不同类型深度范围提前决策算法;然后,检测是否对绘制失真敏感的竖直边缘区域,根据不同的策略进行模式选择搜索;最后,融合这两个算法进一步降低视频编码的复杂度。实验结果表明,所提出的算法在不降低绘制视点质量和编码码率基本不变的情况下,降低了-68.844%编码时间。     

基于自适应窗口的深度视频预处理算法

在自由视点视频系统中,由深度估计算法得 到的深度视频并不精确,对虚拟视点的绘制质量和深度视频的编码效率有很大的影响。本文 提出了一种基于自适应窗口的深度视频预处理算法来提高深度视频的压缩效率和虚拟视点的 绘制质量。首先,将深度视频划分为不连续区域、连续区域中的前景区域和连续区域中的背 景区域。然后,提取出深度视频各区域中的对象边界。最后对不连续区域中的非边界区域进 行高斯滤波,降低深度值骤变对虚拟视点绘制质量的影响,对连续区域中的非边界区域采用 自适应窗口滤波平滑处理以提高压缩效率。实验结果表明,提出的算法可以使深度视频的编 码码率节省8.33~34.39%,同时可使绘制的虚拟视点质量平均提高0.21dB。和Silva的算法相比,在更具实际意义的低码率 端编码效率更显著,绘制结果中对象边界得到了更好的保护。     

计算能力可伸缩的运动估计率失真优化

不同硬件设备具有不同的计算能力,能否在任意给定计算能力约束下达到最好的编码效率,是当前视频编码研究领域的一个极具挑战性问题.同时,随着分块结构越来越灵活的编码标准不断出现（如：HEVC,H.264等）,运动估计不得不反复地应用在大小不同的各种分块上,导致其对编码总体计算复杂度的影响愈加重要.在此背景下,本文提出了一种针对运动估计的计算能力可伸缩（Complexity scalable）优化算法.我们通过对运动估计过程中预测失真度和计算复杂度的变化规律建模,发现根据各宏块的特性设置不同的预测失真度阈值可以优化地分配计算资源.而该阈值的大小则恰恰是各宏块的最小预测失真度加上一个由复杂度约束统一决定的偏移量.有鉴于此,我们进一步构造了计算能力可伸缩的优化运动估计算法,在不增加额外计算量的前提下,快速地得到各个宏块所对应的优化阈值,并完成运动估计.通过实验分析,该算法不仅具备自动适应不同计算复杂度约束的能力,而且在任意给定的复杂度约束下,都能提供优化的编码性能.     

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。     

基于自适应量化矩阵的硬判决量化算法

在视频编码中,视频量化一般分为硬判决量化(HDQ)和软判决量化(SDQ),HDQ与SDQ相比,编码性能虽有所损失,但其编码复杂度低,易于硬件实现的优点依旧是主流编码器所主要采用的量化算法.人眼具有对图像中的高频细节不敏感的特性.因此,基于Bayes最小误判概率约束,离线构建基于视频内容自适应的量化矩阵,在模拟感知SDQ算法机理下,对高频低频分量采用不同的量化步长,提高视频的主观质量和HDQ算法性能.仿真实验表明,相比于传统的HDQ算法,该文算法能达到平均5.048％的码率节省,其中WVGA和WQVGA格式平均达到10.65％的码率节省.相比于感知SDQ算法,平均码率增加仅有1.464％;算法复杂度方面,编码一帧的时间相比于感知SDQ节省了32.956％.     

基于感知的多视点视频编码宏块模式选择快速算法

多视点视频编码(MVC)采取可变块模式选择技术和 多参考帧技术显著提高了编码的压缩效率,但同时带来了巨大的 编码计算复杂度。为了降低MVC的计算复杂度,提出基于感知的快速MVC宏块模式选择 算法。基于人眼视觉感知的特点,利用视觉恰可察觉失真(JND)的概念建立MVC宏块的最优模 式和JND的联系,并利用该联系确定早期结束最优宏块模式选择过程的阈值,根据当前编码 宏块的JND与阈值的 关系自适应地减少每个编码宏块的模式搜索次数,进而减少MVC的方向搜索和参考帧搜索的 次数,以降 低编码的复杂度,提高MVC速度。实验结果显示,对于不同运动特性、内容、纹理信息、相 机间距和图像 尺寸的测试序列,提出的快速算法在率失真性能几乎不变的情况下平均节约76.00% 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.     

一种基于视觉显著度模型的无线视频码率控制算法(英文)

In order to further improve the efficiency of video compression, we introduce a perceptual characteristics of Human Visual System (HVS) to video coding, and propose a novel video coding rate control algorithm based on human visual saliency model in H.264/AVC. Firstly, we modifie Itti's saliency model. Secondly, target bits of each frame are allocated through the correlation of saliency region between the current and previous frame, and the complexity of each MB is modified through the saliency value and its...     

A fast algorithm based on gray level co-occurrence matrix and Gabor feature for HEVC screen content coding

To reduce the computational complexity of screen content video coding (SCC), a fast algorithm based on gray level co-occurrence matrix and Gabor feature model for HEVC-SCC, denoted as GGM, is proposed in this paper. By studying the correlation of non-zero number in gray level co-occurrence matrix with different partitioning depth, the coding unit (CU) size of intra coding can be prejudged, which selectively skips the intra prediction process of CU in other depth. With Gabor filter, the edge information reflecting the features of screen content images to the human visual system (HVS) are extracted. According to Gabor feature, CUs are classified into natural content CUs (NCCUs), smooth screen content CUs (SSCUs) and complex screen content CUs (CSCUs), with which, the calculation and judgment of unnecessary intra prediction modes are skipped. Under all-intra (AI) configuration, experimental results show that the proposed algorithm GGM can achieve encoding time saving by 42.13% compared with SCM-8.3, and with only 1.85% bit-rate increasement.     

A two-pass rate control algorithm for H.264/AVC high definition video coding

In this paper, we propose a novel two-pass rate control algorithm to achieve constant quality for H.264/AVC high definition video coding. With the first-pass collected rate and distortion information and the built model of scene complexity, the encoder can determine the expected distortion which could be achieved in the second-pass encoding under the target bit rate. According to the built linear distortion-quantizer (D-Q) model, before encoding one frame, the quantization parameter can be solved to realize constant quality encoding. After encoding one frame, the model parameters will be updated with linear regression method to ensure the prediction accuracy of the quantization parameter of next encoded frame with the same coding type. In order to obtain the expected distortion of each frame under the target bit rate, a GOP-level bit allocation scheme is also designed to adjust the target bit rate of each GOP based on the scene complexity of the GOP in the second-pass encoding. In addition, the effect of scene change on the updating of D-Q model is considered. The model will be re-initialized at the scene change to minimize modeling error. The experimental results show that compared with the latest two-pass rate control algorithm, our proposed algorithm can significantly improve the bit control accuracy at comparable coding performance in terms of constant quality and average PSNR. On average, the improvement of bit control accuracy achieved about 90%.     

Fast sample adaptive offset algorithm for 360-degree video coding

360-degree video is becoming popular with the development of the virtual reality(VR) technology in recent years. For this kind of video, pictures are projected to a two-dimensional plane. Standard video encoders are used for compression. Due to the projection mapping of the spherical video to the two-dimensional plane video, many conventional coding tools can be adjusted properly for better performance. As the 360-degree videos are high resolution videos, usually 8K resolution videos, the high complexity for encoding 360-degree videos is a problem to be solved urgently, especially for the latest video standard H.265/HEVC. Sample adaptive offset (SAO) is a new tool adopted in H.265/HEVC. It can reduce ringing artifacts and achieve higher coding efficiency. But it also introduces high computational complexity, especially on high resolution videos. In this paper, a fuzzy control based fast SAO method is proposed to reduce the high computational complexity in SAO encoding process according to the characteristics of the ERP format video. Compared with the original algorithm in the reference software HM 16.14, experimental results show that the proposed method can reduce about 73% SAO encoding time on average under the common test conditions with negligible loss.     

Improved motion estimation time using a combination of dynamic reference frame selection and residue-based mode decision

Motion estimation using multiple reference frames is widely used as the basis for recent video coding standards (eg. H.264/AVC) to achieve increased coding efficiency. However, this increases the complexity of the encoding process. In this paper, a new technique for efficient motion estimation is proposed. A combination of multiple reference frame selection and image residue-based mode selection is used to improve motion estimation time. By dynamic selection of an initial reference frame in advance, the number of reference frames to be considered is reduced. In addition, from examination of the residue between the current block and reconstructed blocks in preceding frames, variable block size mode decisions are made. Modified initial motion vector estimation and early stop condition detection are also adopted to speed up the motion estimation procedure. Experimental results compare the performance of the proposed algorithm with a state of the art motion estimation algorithm and demonstrate significantly reduced motion estimation time while maintaining PSNR performance.