En-Route Transcoding缓存的优化放置和替换

对en-route transcoding缓存中的缓存路由和协同放置及替换问题进行了研究.提出了CCRA(cost-aware cache routing algorithm)缓存路由算法,能以可控的探测开销来发现潜在的、具有最小访问开销的缓存对象.在此基础上,建立了en-route transcoding缓存的分析模型,将缓存放置和替换问题形式化为一个最优化问题,并利用一种基于动态规划的方法来求解最佳缓存放置策略.仿真结果表明,与已有的元算法放置策略相比,该协同放置和替换策略可以获得更好的CSR性能.     

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%.     

Motion mapping and mode decision for MPEG-2 to H.264/AVC transcoding

This paper describes novel transcoding techniques aimed for low-complexity MPEG-2 to H.264/AVC transcoding. An important application for this type of conversion is efficient storage of broadcast video in consumer devices. The architecture for such a system is presented, which includes novel motion mapping and mode decision algorithms. For the motion mapping, two algorithms are presented. Both efficiently map incoming MPEG-2 motion vectors to outgoing H.264/AVC motion vectors regardless of the block sizes that the motion vectors correspond to. In addition, the algorithm maps motion vectors to different reference pictures, which is useful for picture type conversion and prediction from multiple reference pictures. We also propose an efficient rate-distortion optimised macroblock coding mode decision algorithm, which first evaluates candidate modes based on a simple cost function so that a reduced set of candidate modes is formed, then based on this reduced set, we evaluate the more complex Lagrangian cost calculation to determine the coding mode. Extensive simulation results show that our proposed transcoder incorporating the proposed algorithms achieves very good rate-distortion performance with low complexity. Compared with the cascaded decoder-encoder solution, the coding efficiency is maintained while the complexity is significantly reduced.

H.264/AVC baseline profile to MPEG‐4 visual simple profile transcoding to reduce the spatial resolution

In this study, we propose a heterogeneous transcoding method of converting an H.264/Advanced video coding (AVC) Baseline profile (BP) video bitstream into an MPEG‐4 Visual simple profile (VSP) video bitstream. The proposed method reduces the spatial resolution for mobile terminals, which support only low resolution video bitstreams. When the H.264/AVC BP video bitstream is transformed into the MPEG‐4 VSP video bitstream, the conversions between the H.264/AVC BP block types and the MPEG‐4 VSP block types are performed by analyzing the macroblocks (MBs) conversion probability and calculating the difference values of motion vector. The proposed transcoding method runs on average 5.5 times faster than the cascaded transcoding methods, for a degradation of the PSNR (peak‐signal‐to ratio) of less than 0.5 dB. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 24–33, 2006     

网络视频监控系统的应用和发展

阐述目前国内外网络视频监控系统技术的发展和应用状况．对视频压缩编码和视频存储两方面关键技术进行分析和比较。     

一种快速的H.264到AVS帧内模式决策算法

利用从H．264解码中得到的帧内预测方向和非零DCT系数来估计AVS编码中的预测方向,以减少AVS帧内预测所需的时间。仿真结果显示新算法可以在保持较好图像质量的同时节省至少56％的运算量。     

iMobile EE – An Enterprise Mobile Service Platform

iMobile is an enterprise mobile service platform that allows resource-limited mobile devices to communicate with each other and to securely access corporate contents and services. The original iMobile architecture consists of devlets that provide protocol interfaces to different mobile devices and infolets that access and transcode information based on device profiles. iMobile Enterprise Edition (iMobile EE) is a redesign of the original iMobile architecture to address the security, scalability, and availability requirements of a large enterprise such as AT&T. iMobile EE incorporates gateways that interact with corporate authentication services, replicated iMobile servers with backend connections to corporate services, a reliable message queue that connects iMobile gateways and servers, and a comprehensive service profile database that governs operations of the mobile service platform. The iMobile EE architecture was also extended to provide personalized multimedia services, allowing mobile users to remotely control, record, and request video contents. iMobile EE aims to provide a scalable, secure, and modular software platform that makes enterprise services easily accessible to a growing list of mobile devices roaming among various wireless networks.     

省略B帧运动补偿的转码算法实现 MPEG-2到MPEG-4视频流转码

MPEG-2和MPEG-4作为最流行的通用编码国际标准,将长期并存于不同的系统和网络服务中.为实现不同视频系统和网络间对以上两种压缩标准的兼容性,本文在详细分析它们压缩算法运算复杂度的基础上,提出三种压缩视频流转码算法.重点讨论了两种高性能转码算法的原理与实现过程,它们均是运算复杂性和视频质量之间的折衷方案:一是针对演播室和后处理环境的最高目标视频质量要求的运动参数重用算法;另一个是针对网络服务器和用户终端系统处理性能有限,具有最小延时要求的实时低复杂度转码算法.大量测试序列的仿真结果证明了算法的良好性能.     

基于GPU的视频转码技术研究

视频转码是个复杂的过程,它需要对已经压缩过的码流进行解析,然后经过处理转换成满足解码终端要求的目标格式码流。为了提高视频转码的效率并降低视频转码的计算复杂度,根据视频转码的要求和图形处理器的并行结构,提出了一种利用GPU强大的并行计算能力来加速视频转码的算法。该算法将视频转码过程中耗时最多、最复杂的运动估计和模式选择过程转移到GPU上并行执行。在开发GPU通用计算能力的时候,采用NVIDIA公司的CUDA(统一计算设备架构)计算平台。实验结果证明,该算法可以有效提高视频转码的速度和效率。     

MDVQM: A novel multidimensional no-reference video quality metric for video transcoding

In this paper, we study the impact of quantization, frame dropping and spatial down-sampling on the perceived quality of compressed video streams. Based on the analysis of quality ratings obtained from extensive subjective tests, we propose a no-reference metric (named MDVQM) for video quality estimation in the presence of both spatial and temporal quality impairments. The proposed metric is based on the per-pixel bitrate of the encoded stream and selected spatial and temporal activity measures extracted from the video content. All the values required to compute the proposed video quality metric can be obtained without using the original reference video which makes the metric for instance useful for making transcoding decisions in a wireless video transmission scenario. Different from comparable metrics in the literature, we have also considered the case when both frame rate and frame size are changed simultaneously. The validation results show that the proposed metric provides more accurate estimation of the video quality than the state of the art metrics.