This paper introduces a variable bit-rate (VBR)MPEG video compression encoder, and evaluates the performance of a statistically multiplexed ATM network supporting a number of such VBR video sources. Bit-rate characteristics obtained from a detailed simulation are provided for a V B R MPEG encoder for CCIRGOI video (operating in the 5-10 Mbps regime) appropriate for medium quality multimedia or broadcasting applications. The results presented include bitrate traces and signal-to-noise ratio data for typical test sequences, along with summary statistics such as the marginal distribution of frame rate. This characterization of the VBR MPEG encoder is followed by summary data from a study of statistical multiplexing on an A T M network. Simulation results for an ATM statistical multiplexer with N>>1 V B R MPEG sources cre presented in terms of key performance measures such as cell loss rate and delay vs. throughput. The results confirm that good ATM channel efficiencies (-PO-SO%) can be obtained at reasonable cell loss rate nn,d delay levels in the statistical multiplexing scenario under consideration.
Princeton, NJ 08540 TES (Transform-Expand-Sample) is a versatile methodology for modeling general stationary time series, and particularly those that are autocorrelated. From the viewpoint of Monte Carlo simulation, TES represents a new and flexible input analysis approach. The salient feature of TES is its potential ability to simultaneously capture first-order and second-order properties of empirical time series (field measurements): Given an empirical sample, TES is designed to fit an arbitrary empirical marginal distribution (histogram) and to simultaneously approximate the leading empirical autocorrelations. Practical TES modeling is computationally intensive and can be effectively carried out only with computer support. A software modeling environment, called TEStool, has been designed and implemented to support the TES modeling methodology, through an interactive heuristic or algorithmic search approach employing extensive visualization. The purpose of this paper is to introduce TES modeling, and to offer some illustrative examples from a range of applications, including source modeling of compressed video and fault arrivals, financial modeling and texture generation. These examples demonstrate the efficacy and versatility of the TES modeling methodology, and underscore the high fidelity attainable with TES models.
TES (Transform-Expand-Sample)is a versatile methodology for modeling general stationary time series, and particularly those that are autocorrelated. The salient feature of TES lies in its ability to simultaneously capture firstorder and second-order properties of empirical time series ; given a sample data sequence, TES is designed to simultaneously capture any arbitrary marginal distribution and approximate the leading autocorrelations. Practical TES modeling is computationally intensive and can be effectively carried out only with software support. A computerized modeling environment, TEStool, has been designed to support the TES modeling methodology, through an interactive heuristic search approach facilitated by stateof-the-art data visualization techniques. The purpose of this paper is to present four examples of the effective use of the TES methodology to model various types of time series that arise in a variety of disciplines, ranging from manufacturing to financial modeling, with particular emphasis on video compression. These examples serve to highlight the efficacy and versatility of the TES modeling methodology.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.