【24h】

Threshold autoregressive models for VBR MPEG video traces

机译:VBR MPEG视频轨迹的阈值自回归模型

获取原文

摘要

Variable bit rate (VBR) Moving Picture Experts Group (MPEG) coded full-motion video traffic is modeled by a nonlinear time-series process. The threshold autoregressive (TAR) process is of particular interest. The TAR model is comprised of a set of autoregressive (AR) processes that are switched between amplitude sub-regions. To model the dynamics of the switching between the sub-regions a selection of amplitude dependent thresholds and a delay value is required. To this end, an efficient and accurate TAR model construction algorithm is developed to model VBR MPEG-coded video traffic. The TAR model is shown to accurately represent statistical characteristics of the actual full-motion video trace. Furthermore, in simulations for the bit-loss rate actual and TAR traces show good agreement.
机译:可变比特率(VBR)运动图片专家组(MPEG)编码的全动态视频流量是通过非线性时间序列过程建模的。阈值自回归(TAR)过程特别令人关注。 TAR模型由一组在幅度子区域之间切换的自回归(AR)过程组成。为了对子区域之间的切换的动力学进行建模,需要选择依赖于幅度的阈值和延迟值。为此,开发了一种有效且准确的TAR模型构建算法,以对VBR MPEG编码的视频流量进行建模。 TAR模型显示为准确代表实际的全动态视频轨迹的统计特征。此外,在模拟比特丢失率时,实际迹线和TAR迹线显示出良好的一致性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号