一种基于分层率失真优化的容错性视频转码算法

容错性视频转码能够在已压缩视频流中嵌入容错性工具,以增强视频流面向无线信道的抗误码能力.但是容错性工具的嵌入在降低失真的同时也会增加码率,因此需要进行率失真优化.针对这一问题,文中提出了一种基于分层率失真优化的容错性转码算法.该算法通过在帧层和宏块层分别对重同步标记的插人和帧内宏块的刷新进行不同粒度的优化来提高算法的灵活性,通过在帧层考虑帧内宏块刷新、帧内预测和运动矢量预测的影响来提高算法的精确性,通过在宏块层减少候选模式的数量来降低算法的复杂度.实验表明,该算法与基于单层率失真优化的算法相比,信噪比可获得0.6～1.1dB的增益,复杂度最多可降低25%.     

像素级运动矢量场边信息生成算法

在分布式视频编码（DVC）中,解码端生成的边信息的质量对整个系统的率失真(RD)性能有着重要影响。为了提高边信息的质量,进而提升DVC系统的RD性能,提出一种基于像素级运动矢量场的边信息生成算法(SGPMVF)。首先,通过运动估计分别获得前后两关键帧的像素级运动矢量场;然后,为Wyner-Ziv帧的每个像素选择合适的运动矢量;最后,通过帧间内插得到高质量的边信息。算法中提出基于SAD（绝对误差和）差值的搜索范围确定方法与像素级运动矢量选择方法。实验结果表明,对于快速运动的序列,该算法在不增加编码复杂度和码率的情况下使边信息的PSNR最大提高1.45 dB,并使DVC系统的RD性能提高0.3～0.7 dB。     

基于H.26L的视频抗误码策略

概括了H．26L视频编码层和网络适应层的主要特性，结合H.26L介绍了帧内刷新、错误掩盖、多参考帧运动估计、DP、FMO等典型的误码控制策略，并针对H．26L典型的FMO抗误码技术与率失真优化算法结合进行了实验仿真，证明FMO的抗误码性能优于一般的Slicc结构，率失真优化有效地提高了视频的鲁棒性能。     

基于多参考帧的多方向边界匹配时域误码掩盖

提出了基于多参考帧的多方向边界匹配时域误码掩盖算法。首先获取误码块相邻宏块的运动矢量,并求出相邻宏块运动矢量的均值,然后根据多方向匹配准则依次求出参考帧的运动矢量,最后选取最佳的运动矢量完成误码掩盖。将该方法与基于多参考帧的误码掩盖算法和多方向边界匹配的误码掩盖算法进行比较,结果表明,该方法效果优于上述两种算法。     

时空域相结合的视频差错掩盖方法

为了克服视频解码端时域差错掩盖技术不能准确估计丢失块运动矢量的缺点,提出了一种将解码端运动估计和隐藏运动模型相结合的误码差错掩盖方法。首先,利用丢失块 周围正确接收像素估计丢失块运动矢量,并计算估计的准确性。然后根据准确性,通过隐藏运动模型充分利用丢失块的空域和时域相关性进行差错块的掩盖。仿真结果表明,与现 有的算法相比,该方法能有效提高重建视频图像质量平均近1dB。     

一种基于精确度的降分辨率视频转码运动矢量合成算法

针对现有的降分辨率视频转码运动矢量合成算法没有考虑输入运动矢量的精确度,从而使得合成误差较高的问题,提出了一种基于精确度的降分辨率视频转码运动矢量合成算法。该算法分为两次合成,首先基于绝对差值和（SAD）对转码输入流中的运动矢量进行平均合成,然后再基于率失真函数,对转码输出流中的相邻运动矢量和第1次合成得到的运动矢量进行选择合成。仿真结果表明,与现有算法相比,该算法在保持较低计算复杂度的情况下,显著降低了运动矢量合成的误差。     

基于运动复杂度的快速模式选择算法

H.264/AVC是ITU-T和ISO/IEC联合制定的最新视频压缩标准。它采用了可变块运动估计和率失真优化模式判决。H.264需要对10 种模式进行率失真优化计算才能得到一个宏块的最优编码模式,极大地增加了编码器的复杂度。为了提高模式选择效率,提出了一种基于运动复杂度的快速模式选择算法。实验结果表明,在PSNR仅有微小下降情况下,该算法可大幅提升编码速度。     

丢包环境中H.264视频编码的快速模式判决

目的 视频编码中传统的快速模式判决算法通常基于对视频源特性的分析,但如果考虑到信道传输中的差错,编码模式的率失真特性就会随之改变,快速模式判决算法的性能也会随之下降,需要考虑丢包环境对快速模式判决算法进行优化。方法 为了解决这一问题,首先分析了丢包环境下各种编码模式的端到端率失真特性,在此基础上提出了一个分层结构的快速模式判决算法。通过快速估计出丢包环境下skip和intra模式的编码率失真代价,进而将模式判决的路径划分为non-intra和non-skip。结果 将本文算法与基于遍历算法的容错视频编码算法进行对比实验,本文算法平均可以降低50%左右的编码时间,同时几乎不会降低率失真性能。结论 实验结果表明,在对丢包环境下端到端率失真代价进行估计的基础上,所提出的分层结构快速模式判决的算法,可以在保证解码端图像质量的同时,显著节省编码时间,满足实时视频通信中对低复杂度和鲁棒性的要求。     

基于分组误差的快速分数像素运动估计算法

H.264是最新一代的视频编码标准,它支持高精度分数像素运动估计。当在整数运动估计中采用快速算法后,分数像素的运动估计可能成为制约快速运动估计算法性能的瓶颈。根据匹配准则的目标函数在最优运动矢量的附近都是单调递增这一假设,提出一种基于分组误差的快速分数像素运动估计算法。实验结果表明该算法不但能够有效降低分数像素运动估计的计算量,而且还能保持较小的率失真性能损失。     

基于相邻运动矢量相关性的运动矢量预测算法

运动矢量的预测是视频编码技术中的一个重要组成部分,它直接关系到运动估计的精度和码率大小。本文提出了一种新的基于运动最相似性的运动矢量预测及其简化算法,通过对相邻块运动矢量的优化,选择运动特征最相似的运动矢量生成预测运动矢量的预测方向缩放因子和预测值。通过在中国音视频编码标准-AVS-中的实现验证和性能比较,这种预测算法相对于中值预测法,系统的平均PSNR值有一定提高,平均码率有所下降。     

Content adaptive fast motion estimation based on spatio-temporal homogeneity analysis and motion classification

In video coding, research is focused on the development of fast motion estimation (ME) algorithms while keeping the coding distortion as small as possible. It has been observed that the real world video sequences exhibit a wide range of motion content, from uniform to random, therefore if the motion characteristics of video sequences are taken into account before hand, it is possible to develop a robust motion estimation algorithm that is suitable for all kinds of video sequences. This is the basis of the proposed algorithm. The proposed algorithm involves a multistage approach that includes motion vector prediction and motion classification using the characteristics of video sequences. In the first step, spatio-temporal correlation has been used for initial search centre prediction. This strategy decreases the effect of unimodal error surface assumption and it also moves the search closer to the global minimum hence increasing the computation speed. Secondly, the homogeneity analysis helps to identify smooth and random motion. Thirdly, global minimum prediction based on unimodal error surface assumption helps to identify the proximity of global minimum. Fourthly, adaptive search pattern selection takes into account various types of motion content by dynamically switching between stationary, center biased and, uniform search patterns. Finally, the early termination of the search process is adaptive and is based on the homogeneity between the neighboring blocks.Extensive simulation results for several video sequences affirm the effectiveness of the proposed algorithm. The self-tuning property enables the algorithm to perform well for several types of benchmark sequences, yielding better video quality and less complexity as compared to other ME algorithms. Implementation of proposed algorithm in JM12.2 of H.264/AVC shows reduction in computational complexity measured in terms of encoding time while maintaining almost same bit rate and PSNR as compared to Full Search algorithm.     

跳帧转码的运动矢量合成研究

由于终端设备、通信网络的异构性，视频服务需要提供跳帧转码功能，而直接使用输入码流运动矢量的视频跳帧算法导致视频质量下降严重．在前向主导运动矢量选择算法的基础上，讨论了运动矢量合成时运动矢量越界和主导帧内块不同处理方法对视频质量的影响，提出基于帧内刷新结构的前向主导运动矢量选择算法．实验结果表明，该算法可以有效阻断错误漂移，降低码率，提高视频质量．     

一种通用的视频传输端到端失真度估算方法

在综合考虑差错扩散、差错掩盖策略及信道误码率的基础上,从像素级对端到端的视频传输差错扩散失真情况进行了分析,根据帧间相关系数和掩盖比率,提出了一种适用于各种时域差错掩盖方法的通用传输失真度估算模型.基于包丢失的仿真实验表明,该模型能够较好地近似估计出差错扩散失真情况,可以适应于各种不同的时域掩盖方法.实验还显示,基于该模型的宏块编码模式的帧内更新选择,与传统的R-D模式帧内更新算法相比,达到了较好地抑制差错扩散的效果,可适用于多种时域掩盖方法.     

Sparse basis selection: new results and application to adaptive prediction of video source traffic

Real-time prediction of video source traffic is an important step in many network management tasks such as dynamic bandwidth allocation and end-to-end quality-of-service (QoS) control strategies. In this paper, an adaptive prediction model for MPEG-coded traffic is developed. A novel technology is used, first developed in the signal processing community, called sparse basis selection. It is based on selecting a small subset of inputs (basis) from among a large dictionary of possible inputs. A new sparse basis selection algorithm is developed that is based on efficiently updating the input selection adaptively. When a new measurement is received, the proposed algorithm updates the selected inputs in a recursive manner. Thus, adaptability is not only in the weight adjustment, but also in the dynamic update of the inputs. The algorithm is applied to the problem of single-step-ahead prediction of MPEG-coded video source traffic, and the developed method achieves improved results, as compared to the published results in the literature. The present analysis indicates that the adaptive feature of the developed algorithm seems to add significant overall value.     

Joint Source-Channel Rate-Distortion Optimization for H.264 Video Coding Over Error-Prone Networks

For a typical video distribution system, the video contents are first compressed and then stored in the local storage or transmitted to the end users through networks. While the compressed videos are transmitted through error-prone networks, error robustness becomes an important issue. In the past years, a number of rate-distortion (R-D) optimized coding mode selection schemes have been proposed for error-resilient video coding, including a recursive optimal per-pixel estimate (ROPE) method. However, the ROPE-related approaches assume integer-pixel motion-compensated prediction rather than subpixel prediction, whose extension to H.264 is not straightforward. Alternatively, an error-robust R-D optimization (ER-RDO) method has been included in H.264 test model, in which the estimate of pixel distortion is derived by simulating decoding process multiple times in the encoder. Obviously, the computing complexity is very high. To address this problem, we propose a new end-to-end distortion model for R-D optimized coding mode selection, in which the overall distortion is taken as the sum of several separable distortion items. Thus, it can suppress the approximation errors caused by pixel averaging operations such as subpixel prediction. Based on the proposed end-to-end distortion model, a new Lagrange multiplier is derived for R-D optimized coding mode selection in packet-loss environment by taking into account of the network conditions. The rate control and complexity issues are also discussed in this paper     

基于帧间预测误差扩展的可逆视频水印

为了提高可逆视频水印的嵌入容量和嵌水印视频质量,提出一种使用运动估计和预测误差直方图修改的算法.在嵌入端,根据视频中相邻帧间的内容关系,对每一帧使用运动估计获得其像素的预测误差,生成预测误差直方图,并通过扩展位于直方图峰值点的预测误差来嵌入水印;在每帧的嵌入过程中会产生少量用于提取水印和还原视频的头信息,包括运动向量和边界表等,将其与水印一起嵌入到该帧的参考帧中.在提取端,先获取参考帧中的水印和头信息并还原参考帧,保证提取操作具有正确的上下文,然后根据获取的头信息和还原后的参考帧提取当前帧中的水印.实验结果表明,采用文中算法获得的预测误差直方图具有高度的集中性,并且对像素值的修改十分微小,比其他可逆水印算法具有更大的嵌入容量和更好的嵌水印视频质量.     

一种面向H.264/AVC的运动估计算法

提出了一种使用具有方向偏向性搜索模板和基于预测模式自适应选择运动估计方案的EPZS(enhanced predictive zonal search)改进算法。该算法将运动估计过程与H.264/AVC中多参考帧、多预测模式等编码工具紧密结合,充分利用视频中运动具有高度时空相关性的特点,综合考虑速度、图像质量及压缩效率等方面的性能。实验结果表明,该算法以极小的计算量得到了与全搜索方法接近的效果;与EPZS算法相比,运动估计的处理速度有了较大幅度的提高,同时保持了与之相当、有时甚至更优的率失真性能。此外,该算法具有很好的鲁棒性,并且能够产生平滑的运动矢量场。     

高清立体视频的传输失真估计模型

针对高清立体视频序列分辨率大、宏块信息量少以及网络传输易出现差错的特点,提出一种能够估计高清立体视频端到端传输的失真模型。该模型能够考虑到丢包对后续帧的错误扩散以及立体视频左右视点帧之间的时空相关性,采用递归算法准确地估计失真,并运用时域帧拷贝的错误隐藏方法降低解码端的复杂度。仿真结果表明,失真模型的平均预测误差能控制在6%以内,对于不同特性和分辨率的立体视频序列,在不同网络环境下传输失真估计均有一定的适用性。     

Media Flow Rate Allocation in Multipath Networks

We address the problem of joint path selection and source rate allocation in order to optimize the media specific quality of service in streaming of stored video sequences on multipath networks. An optimization problem is proposed in order to minimize the end-to-end distortion, which depends on video sequence dependent parameters, and network properties. An in-depth analysis of the media distortion characteristics allows us to define a low complexity algorithm for an optimal flow rate allocation in multipath network scenarios. In particular, we show that a greedy allocation of rate along paths with increasing error probability leads to an optimal solution. We argue that a network path shall not be chosen for transmission, unless all other available paths with lower error probability have been chosen. Moreover, the chosen paths should be used at their maximum available end-to-end bandwidth. Simulation results show that the optimal flow rate allocation carefully adapts the total streaming rate and the number of chosen paths, to the end-to-end transmission error probability. In many scenarios, the optimal rate allocation provides more than 20% improvement in received video quality, compared to heuristic-based algorithms. This motivates its use in multipath networks, where it optimizes media specific quality of service, and simultaneously saves network resources at the price of a very low computational complexity.