VBR视频流量的小波包分解及其长时预测  

Long-term prediction for VBR video traffic based on wavelet packet decomposition

作　　者：陈剑[1] 闻英友[1] 赵大哲[1] 刘积仁[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110004

出　　处：《通信学报》2008年第6期34-42,50,共10页Journal on Communications

基　　金：国家自然科学基金资助项目(60602061);国家高技术研究发展计划(“863”计划)基金资助项目(2006AA01Z413)~~

摘　　要：长时预测是VBR视频流量预测领域中的难点问题。针对其时变、非线性以及长相关性等特点,提出一种多尺度分解的VBR视频业务的特征提取方法。选择具有任意多分辨分解特性的小波包,对其进行空间划分并求解适合视频信号特征提取的最优分解基。基于最优基对视频信号进行快速多尺度分解,得到了各级节点的小波系数矩阵,建立了基于最小二乘支持向量机与最小均方的小波系数预测方法。最后,根据预测小波系数,进一步提出了基于小波系数逆变换的视频流量长时预测方法。仿真结果验证了此算法的有效性。Long-term prediction is one of the most difficult problems in the area of VBR video traffic prediction. As to the time variation, non-linearity and long range dependence in VBR video traffic trace, a novel method of feature based on multi-scale decomposition was proposed. On the analysis of the time-frequency distribution characteristics of the video trace, the wavelet packets which have the ＇trait of arbitrary distinction and decomposition are selected. After space partition of wavelet packets, the best wavelet packet basis for feature extraction is picked out. Based on the best basis, it can do fast arbitrary multi-scale WPT （wavelet packet transform）, and obtain each higher dimension wavelet coefficients matrix. And then wavelet coefficients prediction is proposed based on LS-SVM and LMS algorithms. The long-term prediction of VBR video traffic is obtained through reverse wavelet transforms on the predicted wavelet coefficients. Numerical and simulation results are provided to validate the claims.

关 键 词：VBR业务流量 多尺度分解 小波包 长时预测 最小二乘支持向量机 最小均方预测 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]