MPEG VBR video traffic modeling and classification using fuzzy technique

We present an approach for MPEG variable bit rate (VBR) video modeling and classification using fuzzy techniques. We demonstrate that 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 in MPEG VBR video. Th...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2001-02, Vol.9 (1), p.183-193
Main Authors: Qilian Liang, Mendel, J.M.
Format: Article
Language:English
Subjects:
Bayesian methods
Classification
Classifiers
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Image coding
Mathematical models
MPEG encoders
Studies
Telecommunication traffic
Traffic control
Traffic flow
Transform coding
Video compression
Video on demand
Video sequences
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Summary:We present an approach for MPEG variable bit rate (VBR) video modeling and classification using fuzzy techniques. We demonstrate that 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 in MPEG VBR video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation (std) of T/P/B frame sizes when the frame category is unknown. We propose to use type-2 fuzzy logic classifiers (FLCs) to classify video traffic using compressed data. Five fuzzy classifiers and a Bayesian classifier are designed for video traffic classification, and the fuzzy classifiers are compared against the Bayesian classifier. Simulation results show that a type-2 fuzzy classifier in which the input is modeled as a type-2 fuzzy set and antecedent membership functions are modeled as type-2 fuzzy sets performs the best of the five classifiers when the testing video product is not included in the training products and a steepest descent algorithm is used to tune its parameters.
ISSN:1063-6706
1941-0034
DOI:10.1109/91.917124