MPEG VBR video traffic modeling and classification using fuzzy technique

In this paper, we present a new approach for MPEG variable bit rate (VBR) v ideo Illodeling and classification using fuzzy techniques. We demonstrate t hat a type-2 fuzzy membership function, i.e., a Gaussian MF with uncertain variance, is most appropriate to model the log-value of I/P/B frame sizes i n MPEG VER video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation (std) of I/P/B frame sizes when the frame category is unknown. We propose to use type-2 fuzzy logic classifiers (FLCs) to clas sify video traffic using compressed data. Five fuzzy classifiers and a Baye sian classifier are designed for video traffic classification, and the fuzz y classifiers are compared against the Bayesian classifier Simulation resul ts show that a type-2 fuzzy classifier in which the input is modeled as a t ype-2 fuzzy set and antecedent membership functions are modeled as type-2 f uzzy sets performs the best of the five classifiers when the testing video product is not included in the training products and a steepest descent alg orithm is used to tune its parameters.