基于量化步长的视频编码能量率失真模型

针对移动手语视频通信中存在的功率和码率约束问题,提出一种能量感知的视频编码能量率失真(P-R-D)模型。在H.264视频编码软件平台JM8.6上进行模型参数选择,确定以量化步长作为模型参数。根据移动设备的电池剩余能量划分3个能量状态,在不同状态下分别建立相应的功耗、码率及失真模型。实验结果表明,对于典型的手语视频,P-R-D模型能够准确反映视频编码功耗、码率和质量之间的关系,其中功耗模型和码率模型精确度较高,最大预测误差仅为-1.1795%和-7.9268%。     

基于H.264码流的无参考视频质量评价模型

对视频进行质量评价时,为了不对视频序列完全解码,论文提出了一种基于H.264码流的无参考视频评价模型。该模型只需从码流中解析视频的量化参数、编码比特率和"跳跃"块等信息。论文首先确定了量化参数和主观质量之间的关系,然后分析视频的时间复杂度和空间复杂度,最终利用人眼的空间掩盖效应和时间掩盖效应建立一个能反映人眼视觉特性的视频质量评估模型。实验表明该模型计算出的视频序列的质量和视频的主观质量评价有很好的相关性,能用于对H.264视频进行质量评价。     

可重配置处理器的体系结构级功耗模型与分析

按照可重配置处理器的体系结构建立并实现功耗模型;模型对处理器的电路级特性进行抽象,基于体系结构级属性和工艺参数进行静态峰值功耗估算,基于性能模拟器进行动态功耗统计,并实现三种条件时钟下的门控技术;可重配置处理器与超标量通用微处理器相比,在性能方面获得的平均加速比为3.59,而在功耗方面的平均增长率仅为1.48;通过实验还说明采用简单的CC1门控技术能有效地降低可重配置系统的功耗和硬件复杂度;该模型为可重配置处理器低功耗设计和编译器级低功耗优化研究奠定了基础。     

基于CUDA的H.264视频编码实现

H.264视频编码压缩比率高,但计算复杂度高,编码效率低。该文通过分析H.264编码器中各模块的编码性能,提出了基于CUDA编程模型的H.264视频编码并行框架实现方法,对H.264视频编码的各个关键模块进行CUDA实现,有效的提高了编码的速度。     

基于双边信息的分布式视频压缩感知模型研究

针对传统视频编码技术计算量大和复杂度高的缺点,提出一种基于双边信息的分布式视频压缩感知算法。该算法将压缩感知技术与分布式视频编码技术相结合,把视频序列分为Key帧和CS帧,Key帧运用传统的帧内编码和解码,CS帧编码端运用压缩感知编码,解码端运用视频块内与视频块间的双边信息和梯度投影算法进行优化重构。通过双边信息的运动估计和压缩编码器的设计,实现基于双边信息的分布式视频压缩感知模型的构建。仿真结果表明该模型既可以实现高效编码,又可以实现复杂度由编码端向解码端转移,在较低的采样率下,提高视频的压缩能力和传输速度。     

基于高清编码的自适应Slice划分算法

对于多核高清视频实时编码系统,提出一种自适应Slice划分算法。该算法基于码率控制和熵编码复杂度模型,通过Intra预测得到当前编码图像的纹理复杂度分布,预测编码图像的计算复杂度分布,通过自适应Slice划分实现多核间计算复杂度均匀分配,从而提高多核并行编码效率。实验结果表明,与固定宏块数的Slice划分算法相比,该算法能更有效地提高并行加速比。     

视频解码计算复杂度的线性建模理论及在线预测方法

视频解码是一类最典型的多媒体应用,其计算量大、耗能高.现代多媒体计算平台可利用视频解码计算复杂度固有的动态变化特征来自适应地调整所需计算资源,从而节省能耗,其前提是对视频解码计算复杂度进行准确估计.作者基于解码计算复杂度与帧长之间的线性关系,提出了一种利用状态变量法对解码计算复杂度进行理论建模和在线估计的方法.与传统的直接对帧长和计算复杂度之间的输入-输出依赖关系进行建模所不同,这里将视频解码系统表征为由视频内容特征的状态变化所驱动的系统.首先从语义层面对解码器各模块的解码复杂度进行分析,并导出各模块计算复杂度与语义参数间的依赖关系模型,总解码复杂度为各子模块的复杂度之和.经过化简得到解码计算复杂度与帧长之间的线性模型,其中模型系数为上述语义参数的函数,表征了视频内容特征的状态变化,被定义为状态变量.再结合压缩视频流中相邻帧语义参数之间的相关性,将系统状态方程定义为反映视频内容变化程度的分段线性函数.根据Ⅰ帧和P帧状态轨迹特性及其在压缩码流中位置属性的不同,分别进行计算复杂度在线估计:对于I帧,采用统计分析方法获得其状态变量的均值并进行在线估计;而对P帧,则是在运行过程中利用状态方程对状态变量进行实时更新和计算复杂度估计.在基于SimpleScalar的软件仿真平台和基于DSP的嵌入式硬件平台上分别对H.264、MPEG-4压缩码流的解码计算复杂度进行在线估计,实验结果表明:对解码计算复杂度的平均估计误差在7％以内,预测精度非常高,而且状态方程更新过程简单,在线运行复杂度低,特别适用于嵌入式移动设备.     

基于H.264的复杂度-失真最优的运动估计算法

为了满足嵌入式、移动设备的实时视频编码应用，提出一种可分级的复杂度-失真最优的运动估计算法，通过调整复杂度控制参数来实现视频编码器计算复杂度的可分级性，确保视频质量达到最优，在编码效率最高和失真最小间取得相对最佳平衡。实验结果表明，随着处理器计算能力的变化，该算法能自动调整编码器的计算复杂度，大大减少运动估计的运算量，同时图像质量和码率变化不大。     

H.264计算资源和比特资源联合优化分配方法

针对聋哑人移动手语视频通信,提出一种H.264计算资源和比特资源联合优化分配方法。该方法根据聋哑人视觉选择特性将手语视频划分为不同的区域,通过为不同区域分配不同的量化系数进行比特资源优化分配;并将H.264手语视频编码复杂度分为三个级别,根据能量受限设备的电池能量自适应地选择编码级别进行计算资源优化分配。研究结果表明:该算法在保证手语视频感兴趣区编码质量的同时,可以有效降低编码器的计算复杂度,延长电池工作时长。     

立体视频编码中运动和视差联合估计算法的研究

立体视频编码不仅要在同一个视点间进行运动估计,还要在相邻视点间进行视差估计,计算复杂度非常高.如何有效地结合运动估计和视差估计,降低运算复杂度是当前亟待解决的问题.本文详细分析了立体视频左右通道视频流之间的相关性,提出一种运动和视差联合估计快速算法.根据运动—视差模型,结合时空域的多参考帧来进行联合的运动和视差估计,实现对数据的有效编码压缩.实验证明,该算法与传统的全搜索算法相比,能在保证率失真性能基本不变的前提下,降低96.45%的运算复杂度,大大提高编码速度.     

Network-driven low complexity coding for wireless multi-view video system

For wireless multi-view video system, which has very limited capability for storage and computation at the encoder, it is essential to design a video coding scheme with low complexity. Wyner-Ziv (WZ) coding scheme shifts large computational complexities to decoder with side information reconstruction, however, its encoding efficiency is not high enough. To obtain higher encoding efficiency and lower encoding complexity for wireless multi-view video applications, a network-driven low complexity video coding method is proposed in the paper. The proposed method is designed to implement color correction, motion vector extrapolation and disparity extraction at the central node of network, so as to reduce large computational complexities of motion estimation at the encoder and disparity estimation at the decoder. Experimental results show that encoding efficiency of the proposed method is higher than those of WZ coding and H.264 intraframe coding methods. The encoding complexity of the proposed method is greatly decreased and the decoded views are of color consistency, which is favorable to high quality view rendering at the decoder.     

Fast Motion Estimation on Graphics Hardware for H.264 Video Encoding

The video coding standard H.264 supports video compression with a higher coding efficiency than previous standards. However, this comes at the expense of an increased encoding complexity, in particular for motion estimation which becomes a very time consuming task even for today's central processing units (CPU). On the other hand, modern graphics hardware includes a powerful graphics processing unit (GPU) whose computing power remains idle most of the time. In this paper, we present a GPU based approach to motion estimation for the purpose of H.264 video encoding. A small diamond search is adapted to the programming model of modern GPUs to exploit their available parallel computing power and memory bandwidth. Experimental results demonstrate a significant reduction of computation time and a competitive encoding quality compared to a CPU UMHexagonS implementation while enabling the CPU to process other encoding tasks in parallel.     

实时视频系统的数据编码速率智能控制

该文探讨在CDMA无线网络环境下实现更高QoS的实时视频传输，包括了对实时视频编码速率调整的智能控制方法，以及如何对编码速率调整的内容。采用网络特征反馈的样本，将其通过动态神经网络异步输入，经过合适的训练，得到一个确定神经网络参考模型，用该模型对未来的网络环境作出预测，并由预测出来的结果控制数据编码调整方案的一种智能控制手段。通过神经网络来实现对网络特征的捕捉，再作出合适的编码速率调整，这种调整更接近真实需要，更好地满足了实时QoS的实时视频系统要求，使得实时性能得到显著的提高。     

Power-efficient video encoding on resource-limited systems: A game-theoretic approach

Most mobile video applications are often deployed on battery-operated devices. With limited power supply, it is a challenging issue to support video applications with high resolution due to the complex functionality and high resource requirements. Thus, power-efficient design is important in computation intensive applications especially for mobile video terminals. Previous works on power-efficient control in video encoding systems focus on the low complexity design while typically ignoring the impact of scalable design by considering various power consumption involved in the encoding process. This paper is dedicated to developing a power-scalable video encoding (PSVE) strategy for energy-limited mobile terminals. PSVE can help the video encoding terminal to adjust its power consumption budget efficiently so as to enhance the power-scalable capability in mobile video terminals. This paper first establishes game theoretical analysis to model the power consumption problem as a bargaining problem. Then, the tradeoff between encoding effect and power consumption achieved by the use of game theory. The scalable and low power video encoding system based on Nash equilibrium solution is derived through the analysis on the power consumption and encoding effect. Experimental results demonstrate the efficiency of the proposed approach.     

基于单源分段编码的无线对等感知网数据收集模型

针对大规模无线对等感知网络在触发型数据场景下的部分数据收集问题，提出单源分段编码模型。利用单个源节点的多个分段之间编码而成的数据来记录源数据，提高数据可靠性的同时使其适应大规模网络下的部分数据收集。通过使用游走包进行编码操作，避免源节点过多的能量消耗。同时提出针对节点存储空间提出动态划分编码单元策略，利用邻居间的信息交换动态的调整源数据切分的编码单元大小，实现节点存储空间与收集效率的动态调整。并针对灾难场景下的数据收集提出了危险感知编码冗余量动态调整策略，通过对邻居状态的感知动态调整发送的随机游走包的个数，自适应地提升编码冗余量，提高数据恢复率。     

Interactive scheduling for mobile multimedia service in M2M environment

Computational load of motion estimation in advanced video coding (AVC) standard is significantly high and its more true for HDTV sequences. In this paper, video processing algorithm is mapped onto a learning method to improve machine to machine (M2M) architecture, namely, the parallel reconfigurable computing (PRC) architecture, which consists of multiple units, First, we construct a directed acyclic graph (DAG) to represent the video coding algorithms comprising motion estimation. In the future trillions of devices are connected (M2M) together to provide services and that time power management would be a challenge. Computation aware scheme for different machine is reduced by dynamically scheduling usage of multi-core processing environment for video sequence depending up complexity of the video. And different video coding algorithm is selected depending upon the nature of the video. Simulation results show the effectiveness of the proposed method.     

An Adaptive Motion-Based Unequal Error Protection Approach for Real-Time Video Transport Over Wireless IP Networks

In this work, we consider the delivery of digital video over future 3G wireless IP networks and we propose a low-complexity adaptive motion-based unequal error protection (UEP) video coding and transmission system which efficiently combines three existing error-resilience techniques by exploiting knowledge of the source material as well as the channel operating conditions. Given this information, the proposed system can adaptively adjust the operating parameters of the video source encoder and the forward error correction (FEC) channel encoder to maximize the delivered video quality based upon both application-layer video motion estimates and link-layer channel estimates. We demonstrate the efficacy of this approach using the ITU-T H.264 video source coder. The results indicate that a significant performance improvement can be achieved with enhanced resilience to inaccurate channel feedback information and with substantially reduced computational complexity compared to competing approaches.     

Content-adaptive wireless streaming of instructional videos

Video streaming over wireless networks is becoming increasingly important for a variety of applications. To accommodate the dynamic change of wireless network bandwidths, Quality of Service (QoS) scalable video streams need to be provided. This paper presents a system of content-adaptive streaming of instructional (lecture) videos over wireless networks for E-learning applications. We first provide a real-time content analysis method to detect and extract content regions from instructional videos, then apply a “leaking-video-buffer” model to adjust QoS of video streams dynamically based on video content. In content-adaptive video streaming, an adaptive feedback control scheme is also developed to transmit properly compressed video streams to video clients not only based on network bandwidth, but also based on video content and the preferences of users. Finally, we demonstrate the scalability and content adaptiveness of the proposed video streaming system with experimental results on several instructional videos.