Threshold Neural Network Model for VBR Video Trace

가변적 비디오 트랙을 위한 임계형 신경망 모델

  • 장봉석 (목포대학교 정보공학부 멀티미디어공학전공)
  • Published : 2006.02.01


This paper shows modeling methods for VBR video trace. It is well known that VBR video trace is characterized as longterm correlated and highly intermittent burst data. To analyze this, we attempt to model it using neural network with auxiliary linear structures derived from residual threshold. For testing purpose, we generate VBR video trace from chaotic nonlinear function combined with the geometric random noise. The modeling result of the generated data shows that the attempted method represents more accurately than the traditional neural network. However, we also found that combining hRU to the attempted modeling method can yield a closer agreement to statistical features of the generated data than the attempted modeling method alone.


VBR Video;Neural Network;Threshoid;ARMA;Modeling;Training;Time Series