Modeling full-length VBR video using Markov-renewal-modulated TES models


This paper describes a general methodology for constructing accurate source models of long sequences (full-length movies) of full-motion compressed VBR (variable bit rate) video, directly from empirical data sets of observed bit rate (frame size) records. The main idea is to first cluster scenes into classes and use TES (transform-expand-sample) processes to carefully model their duration and bit rate processes, and then modulate these scene class models by a Markov-renewal process that governs scene transitions. The resulting model is a Markov-renewalmodulated TES (MRMT) process. The fidelity criteria imposed are relatively stringent in that they call for the simultaneous capture of empirical histograms and autocorrelation functions, as well as the qualitative appearance of the empirical data, including burstiness. To illustrate the methodology, the paper provides a detailed description of the modeling procedure, using a JPEGlike coding trace of the “Star Wars” movie as a working example. The resulting MRMT model was validated by comparing its simulation-based statistics to their empirical counterparts, and by a performance study of the corresponding loss rates for various buffer sizes. The results support the efficacy of the modeling methodology and suggest that suites of realistic and compact source models can be developed for other types of movies under a variety of coding schemes. These may be used to drive realistic Monte Carlo simulations of broadband telecommunications networks.

DOI: 10.1109/49.700899

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@article{Melamed1998ModelingFV, title={Modeling full-length VBR video using Markov-renewal-modulated TES models}, author={Benjamin Melamed and Dimitrios E. Pendarakis}, journal={IEEE Journal on Selected Areas in Communications}, year={1998}, volume={16}, pages={600-611} }