A survey of statistical source models for variable-bit-rate compressed video
Multimedia Systems - Special issue on video content based retrieval
A gamma-based framework for modeling variable-rate MPEG video sources: the GOP GBAR model
IEEE/ACM Transactions on Networking (TON)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Statistical Modeling of MPEG Coded Video
ICON '01 Proceedings of the 9th IEEE International Conference on Networks
Modeling full-length video using Markov-modulated Gamma-based framework
IEEE/ACM Transactions on Networking (TON)
Genetic algorithm based identification of nonlinear systems bysparse Volterra filters
IEEE Transactions on Signal Processing
AR-based quadratic modeling for GOP MPEG-encoded video traffic in ATM networks
Computer Communications
Traffic models in broadband networks
IEEE Communications Magazine
A general AR-based technique for the generation of arbitrary gamma VBR video traffic in ATM networks
IEEE Transactions on Circuits and Systems for Video Technology
Efficient modeling of VBR MPEG-1 coded video sources
IEEE Transactions on Circuits and Systems for Video Technology
Nested auto-regressive processes for MPEG-encoded video traffic modeling
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
Variable bit rate (VBR) video traffic models, which accurately represent the traffic characteristics and statistical properties of real videos, can provide significant information about expected traffic behavior. This knowledge can be used in the development of effective control schemes and improved network quality of service. An interesting class of models based on the idea of generating a number of chi-square sequences, by passing a Gaussian autoregressive (AR) process through a simple nonlinearity, was recently introduced. The gamma process in this class of models is obtained by a linear combination of the chi-square sequences. This model is simple and allows for arbitrary selection of both the AR model and the shape parameter of the gamma probability density function. However, the AR filter order is chosen mainly on a trial-and-error basis. In addition, while the approach uses a linear combination of K chi-square sequences, it fixes (K - 2) coefficients and solves for only the remaining two because it has more equations than unknowns. Occasionally, the resulting solution is not feasible and additional trials for different solutions are required. It is therefore the objective of this paper to use genetic algorithms to provide a more systematic approach to find the various model parameters. The paper also presents a thorough statistical analysis of the generated synthetic data in order to assess its suitability for representing MPEG video traffic. A comparison with published results is carried out in terms of how close are the means, standard deviations, and the autocorrelation functions to those of the real data. A comparison of over 10000 replications and a number of different video traces reveals that a significant improvement can be achieved in almost all measures and for almost all the movies tested.