一种基于视觉感知的H.264 码率控制算法

针对目前视频编码标准H.264 的码率控制算法未考虑人眼视觉感知、易导致编码 后视频图像质量波动的不足,提出了一种基于视觉感知的H.264 码率控制算法。首先,设计了 像素域的恰可察觉失真模型。在此基础上,根据各帧的恰可察觉失真的大小进行帧层比特分配。 其次,建立了基于结构相似度的率失真模型,并采用此模型设计了基本单元层(basic unit, BU) 的比特分配方案。最后结合二次速率-量化模型得到量化参数。实验结果表明,该算法与目前 H.264 中典型的码率控制算法相比,错误率降低了0.2%。     

基于无线视频传感器网络的失真优化路由算法

针对无线视频传感器网络链路不稳定、重建质量要求不高的特点,提出一种适应多描述编码（MDC）可靠传输的路由算法EDLOR。该算法充分考虑了视频编码速率、时延受限、网络丢包等因素,以多描述峰值信噪比（PSNR）作为优化目标,使视频总体失真最小化;然后根据计算结果,将多描述编码分配到指定的路径进行传输。实验结果表明,EDLOR路由算法能够提高平均PSNR,降低了网络丢包率,提升了总体视频质量。     

基于概率统计理论的全局率失真优化判决策略

介绍了(JVT)ITU-T和VCEG/IEC的MPEG国际视频编码标准组织,最近已经起草的将被称为ITU-T和VCEG/IEC新标准的视频编码H.264。并结合差错信道的视频传输模型,分析了其传输H.264视频压缩数据存在的编码失真、传输误码失真和扩散失真问题,讨论了在H.264标准差错信道下,全局率失真优化判决策略,并结合H.264SEI(SupplementalEnhancementInformation)域的反馈机制和概率统计理论的知识,提出了一种简单估算失真的算法。该模式判决算法较好地改善了抗误码性能。     

一种基于统计的H．264差错信道下抗误码方法

结合在差错信道下的视频传输模型，本文分析了在差错信道下传输H．264视频压缩数据存在的编码失真、传输误码失真及扩散失真问题；针对这个问题，讨论了H．264标准中 基于宏块的误码掩盖技术及差错信道下基于全局率失真优化的宏块模式判决算法；并结合H．264 SEI域的反馈机制和概率统计理论，提出了一种简单估算失真的方法。该模式式判决算法较好地改善了抗误码性能。     

基于Internet网络的视频传输失真分析

基于Internet网络的视频通信必须进行差错控制,而差错控制需要准确估计视频流在Internet网络传输中产生的信道失真。对基于Internet网络的视频传输过程进行了失真分析,结合帧间差错扩散的特点和差错帧之间的相关性,提出一种估计包损失失真的通用模型,通过接收端提供的丢包反馈信息,可以准确估计传输中引入的失真。使用JVT/H.264编码的标准视频测试序列进行验证,该模型对信道失真的估计精度比在此之前所提出的其它模型有明显提高。     

一种抗H.264压缩的低比特率视频水印算法

为了解决H.264压缩视频版权保护问题,提出一种新的低比特率视频水印算法。根据H.264中DCT系数分布特点,通过系数的符号编码,水印信息被嵌入到I帧DCT量化系数中。引入量化失真补偿的办法,降低了水印嵌入时引起的视频失真。水印检测时采用了符号相关检测思想,不需要原视频作参考,实现了盲检测功能。实验结果显示,新算法在保证较小视频失真的同时,能够很好地抵制H.264压缩攻击。     

基于起始量化参数预测的码率控制优化算法

为了提高H.264/AVC中码率控制算法起始量化参数预测的准确性,提出了一种起始量化参数的预测算法.通过分析视频图像编码前的参数信息和Ⅰ帧复杂度的特征量,提出了初始量化参数(QPo)预测模型,并根据前一帧组的实际码率和量化参数对当前帧组(GOP)量化参数的影响,提出了一种新的帧组起始量化参数(QPst)预测模型,然后用提出模型对现有算法进行改进.实验结果表明,该算法能更为准确的预测起始量化参数,在低比特率情况下,对于视频图像的码率控制精度和率失真优化效果较H.264/AVC参考软件中所使用的JVT-G012码率控制算法有所提高,而且可以获得较高的平均峰值信噪比(PSNR)和稳定的重建图像质量.     

基于RTP的H.264视频流实时打包传输的研究

随着无线宽带网络的快速发展,以及高效的视频压缩技术的应用,流媒体的实时高效传输成为亟待解决的问题。本文从视频传输系统模型入手,分析了最新视频编码标准H.264在算法上的层次结构特点以及音、视频实时传送协议RTP的高效性。并且随着适合H.264流的RTP载荷格式的提出,基于RTP的H.264流媒体无线传输渐渐地得到应用。本文成功实现了一种基于RTP的H.264传输算法,实验通过了TD330无线3G模块测试,并且获得良好的图像质量,实现了低时延、较小丢包率的打包算法。     

基于丢包率预测的视频传输纠错算法

为解决视频图像在互联网中进行传输时,其质量易受网络丢包率、时延等因素的影响而显著降低的问题,提出了一种基于丢包率预测的视频传输纠错算法。该算法采用隐马尔可夫模型预测网络丢包率,根据丢包率的大小自适应地选择FEC或ARQ对视频图像进行纠错操作。当预测出的丢包率较高时,为避免FEC算法在丢包率较高时降低带宽利用率,采用选择性ARQ算法恢复丢失的视频数据包,并通过限制其重传次数使视频传输的实时性得到了保证;当预测出的丢包率较低时,则采用优化了RS冗余值的FEC算法进行纠错操作。在OPNET modeler中进行的仿真实验表明,与HARQ算法相比,使用该纠错算法,视频图像的PSNR的平均值提高了1.6 dB,平均时延减少了0.24 s左右。该算法不但降低了视频传输的平均时延和丢包率,而且提高了接收端视频图像的重建质量,具有复杂度低、实现简单的特点。     

H.264在网络视频监控系统中的应用研究

由于具有压缩率高、图像失真率低、网络适应性强的特点,H.264已经成为网络视频监控系统应用研究中的热门。针对视频压缩效率和实时传输之间的矛盾,以及视频图像质量要求的提高,根据H.264和网络监控系统的技术原理,分析了H.264在网络视频监控系统中的应用优势和可行性,旨在提出一套基于H.264的网络监控系统解决方案。     

基于视频业务质量优化的认知无线电传输信道选择

认知无线电网络中,次级用户选择信道的传统技术基于信道特性对传输信道进行随机选择,忽略了应用层视频业务对信道质量的要求。针对该问题提出了一种基于视频业务质量优化的信道选择技术,优化视频业务端到端的传输质量。通过最小化端到端视频失真,跨层优化综合选择物理层传输信道、自适应调制与编码模式以及应用层的编码量化参数。该方法在多信道认知无线电网络下进行了大量的视频传输仿真模拟实验。实验结果表明该方法能够比不含信道选择的跨层优化方法提高认知无线电网络下次级用户的视频传输业务客观质量1.5 dB以上。     

Low-Delay Low-Complexity Bandwidth-Constrained Wireless Video Transmission Using SVC Over MIMO Systems

We propose an efficient strategy for the transmission of scalable video over multiple-input multiple-output (MIMO) wireless systems. In this paper, we use the latest scalable H.264 codec (SVC), which provides combined temporal, quality and spatial scalability. At the transmitter, we estimate the decoded video distortion for given channel conditions taking into account the effects of quantization, packet loss and error concealment. The proposed scalable decoder distortion algorithm offers low delay and low complexity. The performance of this method is validated using experimental results. In our proposed system, we use a MIMO system with orthogonal space-time block codes (O-STBC) that provides spatial diversity and guarantees independent transmission of different symbols within the block code. The bandwidth constrained allocation problem considered here is simplified and solved for one O-STBC symbol at a time. Furthermore, we take the advantage of the hierarchical structure of SVC to attain the optimal solution for each group of pictures (GOP) of the video sequence. We incorporate the estimated decoder distortion to optimally select the application layer parameter, i.e., quantization parameter (QP), and physical layer parameters, i.e., channel coding rate and modulation type for wireless video transmission.      

Bandwidth allocation for video transmission with differentiated quality of experience over wireless networks

Delivering digital video content with enhanced quality of experience to the end users over error-prone multi-hop wireless networks is a challenging issue. In video transmission over such wireless networks, many network-based (packet loss, delay, etc.) and source-based (encoding quantization level etc.) parameters can impose some levels of impairment on the perceived video quality. In a video quality enhancement strategy, accurate video quality metrics play a crucial role in the designing process of optimal rate (bandwidth) allocation algorithms. Many cross-layer optimization (CLO) based rate allocation strategies have been developed for this purpose which consider different objective functions (congestion level, total packet loss, etc.) in wireless networks. The main contributions of the proposed work are twofold. At first, an optimal bandwidth allocation framework is being developed in which based on some network-specific constraints and by incorporating an accurate video quality metric, the total weighted quality of experience of some competing video sources is being optimized bases on cross-layer design techniques. Secondly, these optimal rates have been used for differentiated Quality of Experience (QoE) enforcement between multiple competing video sources. The resulting optimal rates can be used as rate-feedbacks for on-line rate adaptation of a moderate video encoder such as MPEG4. The aforementioned weight parameters are selected based on the importance of each video sequence’s quality and can be associated with some previous service level agreement (SLA) based prices. Some numerical analysis have been presented to validate the theoretical results and to verify the claims.     

Fine-Granularity Transmission Distortion Modeling for Video Packet Scheduling Over Mesh Networks

Packet scheduling is a critical component in multi-session video streaming over mesh networks. Different video packets have different levels of contribution to the overall video presentation quality at the receiver side. In this work, we develop a fine-granularity transmission distortion model for the encoder to predict the quality degradation of decoded videos caused by lost video packets. Based on this packet-level transmission distortion model, we propose a content-and-deadline-aware scheduling (CDAS) scheme for multi-session video streaming over multi-hop mesh networks, where content priority, queuing delays, and dynamic network transmission conditions are jointly considered for each video packet. Our extensive experimental results demonstrate that the proposed transmission distortion model and the CDAS scheme significantly improve the performance of multi-session video streaming over mesh networks.     

Classification-Based System For Cross-Layer Optimized Wireless Video Transmission

Joint optimization strategies across various layers of the protocol stack have recently been proposed for improving the performance of real-time video transmission over wireless networks. In this paper, we propose a new, low complexity system for determining the optimal cross-layer strategies for wireless multimedia transmission based on classification and machine learning techniques. We first determine offline the optimal cross-layer strategy for various video sequences and channel conditions (training data). Subsequently, we extract relevant and easy to compute content features, encoder-specific parameters, and channel resources from the training data, and train a statistical classifier based on these optimal results. At run-time, we predict using the classifier the optimal cross-layer compression and transmission strategy using these simple, on-the-fly computed features. Hence, we consider the complex problem of finding the optimal cross-layer strategy during the training phase only, and rely at transmission-time on low-complexity classification techniques. We illustrate the proposed classification-based system by performing MAC-application layer optimizations for video transmission over 802.11a wireless LANs. Specifically, we predict the optimal MAC retry limits for the various video packets and compare our results against both optimal and conventionally used ad-hoc cross-layer solutions. Our results indicate that considerable improvements can be obtained through the proposed cross-layer techniques relying on classification as opposed to optimized ad-hoc solutions. The improvements are especially important at high packet-loss rates (5% and higher), where deploying a judicious mixture of strategies at the various layers becomes essential. Furthermore, our proposed classification-based system can be easily modified to include other layers from the OSI stack during the cross-layer optimization.     

SSIM-based joint-bit allocation for 3D video coding

The quality of a 3D video display depends on virtual view synthesis process which is affected by the bit allocation criterion. The performance of a bit allocation algorithm is dependent on various encoding parameters like quantization parameter, motion vector, mode selection, and so on. Rate-distortion optimization (RDO) is used to efficiently allocate bits with minimum distortion. In 3D video, rate-distortion (RD) property of synthesized view is used to assign bits between texture video and depth map. Existing literature on bit allocation methods use mean square error (MSE) as distortion metric which is not suitable for measuring perceptual quality. In this paper, we propose structural similarity (SSIM)-based joint bit allocation scheme to enhance visual quality of 3D video. Perceptual quality of a synthesized view depends on texture and depth map quality. Thus, SSIM-based RDO is performed on both texture and depth map where SSIM is used as distortion metric in mode decision and motion estimation. SSIM-based distortion model for synthesized view is determined experimentally. As SSIM cannot be related to quantization step, SSIM-MSE relation is used to convert distortion model in terms of MSE. The Lagrange multiplier method is used to solve the bit allocation problem. The proposed algorithm is implemented using 3DV-ATM as well as HEVC. RD curves show reduction in bitrate with an improvement in SSIM of synthesized view.     

基于GM(1,1)模型的自适应链路层ARQ控制策略

提出了一种用于无线实时流媒体传输的自适应链路层ARQ控制策略,用以提高接收方的播放质量。该策略采用跨层设计的方法,基于GM（1,1）模型预测当前的网络状态,考虑GOP可解码帧数的特性,自适应地调整ARQ参数Nmax;另一方面,在应用层采用自适应FEC策略,在视频源数据和冗余数据之间动态分配网络带宽。数学分析和仿真验证均表明,该策略能使接收方获得最大的可播放帧率,有效地提高了流媒体传输的可靠性和实时性。     

面向无线网络的可伸缩视频编码传输策略*

针对现有面向无线网络的可伸缩视频编码(Scalable Video Coding, SVC)传输策略未能充分考虑失真和能耗的问题,提出了一种基于失真和节点能耗最小化的SVC传输策略。该策略在分析SVC的编码失真、传输过程中的丢包失真的基础上,计算了接收端的视频失真总和;通过计算SVC传输系统的功率,对无线网络中的节点能耗进行了分析。然后综合考虑了能耗、传输时间及质量要求,将SVC的传输策略转化为一个优化问题,进而得到最优的SVC编码参数,在获得较优视频质量的前提下实现了SVC的可靠传输。仿真实验结果表明,与目前典型的SVC传输策略相比,该策略不但有效降低了SVC传输过程中的平均失真,而且在相同的能量消耗水平下,获得了更好的视频质量。     

PABM-EDCF: parameter adaptive bi-directional mapping mechanism for video transmission over WSNs

To support and keep high quality of video transmission over wireless sensor networks, this paper proposes a parameter adaptive bi-directional cross-layer mapping algorithm on the basis of the operation mechanism of IEEE 802.11e Enhanced Distributed Coordination Function (EDCF) supporting video service differentiation, named PABM-EDCF. Instead of classifying video data to a specific access category in 802.11e network, our proposed adaptive cross-layer scheme makes use of the hierarchy characteristic of video stream, dynamically maps video data to the appropriate access categories according to both the significance of the different video frames and the network traffic load. The significance passes from the application layer to the media access layer through a cross-layer architecture. In order to prevent the network congestion and keep the high transmission quality, the proposed algorithm adopts bi-directional floating mapping algorithm and congestion awareness mechanism based on the queue length and frame types. The mapping parameters are updated according to the network condition in time. Our simulation results indicate: the proposed method (a) improves the video transmission quality; (b) optimizes the management and utilization of queue resources; and (c) yields superior performance (under different loads) over 802.11e, static mapping and adaptive mapping schemes.     

Wireless multimedia delivery over 802.11e with cross-layer optimization techniques

The use of wireless networks has spread further than simple data transfer to delay sensitive and loss tolerant multimedia applications. Over the past few years, wireless multimedia transmission across Wireless Local area Networks (WLANs) has gained a lot of attention because of the introduction of technologies such as Bluetooth, IEEE 802.11, 3G, and WiMAX. The IEEE 802.11 WLAN has become a dominating technology due to its low cost and ease of implementation. But, transmitting video over WLANs in real time remains a challenge because it imposes strong demands on video codec, the underlying network, and the Media Access Control (MAC) layer. This paper presents a cross-layer mapping algorithm to improve the quality of transmission of H.264 (a recently-developed video coding standard of the ITU-T Video Coding Experts Group) video stream over IEEE 802.11e-based wireless networks. The major goals of H.264 standard were on improving the rate distortion and the enhanced compression performance. Our proposed cross-layer design involves the mapping of H.264 video slices (packets) to appropriate access categories of IEEE 802.11e according to their information significance. We evaluate the performance of our proposed cross-layer design and the results obtained demonstrate its effectiveness in exploiting characteristics of the MAC and application layers to improve the video transmission quality.