摘要
该文分析了已有的 MPEG VBR视频流模型及其缺点 ,并在此基础上对 VBR视频流的统计特性进行了研究 .实验结果表明 :通过对整个视频流进行场景划分 (聚类 ) ,聚类间用 Markov调制链建模 ,而每一聚类中独立的场景则可以采用 TES模型基于 GOP(Group Of Picture)而非帧进行建模 ,则该方法既能避免状态空间过大 ,又能避免帧间周期性的自相关 ,因而能够更好地拟合 VBR视频流序列的一阶和二阶统计特性 .同时 ,对独立场景的 GOP分布函数可以采用 Gamm a函数进行拟合 ,自相关函数则可以采用双指数函数更好地拟合 .
We analyze the statistical characteristics of MPEG VBR video stream. Our research shows, by clustering the video sequence into independent classes, the state space of Markov chain can be greatly decreased, so all classes can be modeled by a Markov modulated chain. In addition, individual class can be modeled by TES model based on GOP bit rate in order to avoid the periodic correlation of frames. The first order (distribution) and second order (autocorrelation) statistical characteristics of scenes in every individual class can be easily fitted by Gamma function and double exponential function, which even more simplifies the modeling complexity. Our research result is useful to multimedia flow modeling, network performance analysis, B-ISDN design and network control strategy such as congestion control, call admission control (CAC), dynamic multiplexing, etc.
出处
《计算机学报》
EI
CSCD
北大核心
2001年第9期1002-1008,共7页
Chinese Journal of Computers
