Paper Info
Title
Automated TES modeling of compressed video
Abstract
TES (transform-expand-sample) is a versatile class of stationary stochastic processes which can model arbitrary marginals, a wide variety of autocorrelation functions, and a broad range of sample path behaviors. TES models include one set of parameters for exact fitting of the empirical distribution (histogram), and another for approximating the empirical autocorrelation function. The former is easy to determine algorithmically, but the latter involves a hard heuristic search on a large parametric function space. This paper describes an algorithmic procedure which largely automates TES modeling. The algorithm is cast in a nonlinear programming setting with the objective of minimizing a weighted square distance between the empirical autocorrelation function and its candidate TES-model counterpart. It combines a brute-force search with a steepest-descent nonlinear programming technique, and it performs well owing to the simplicity of the constraints and the nice local behavior of the objective function. Finally, we illustrate the efficacy of our approach via two examples from the domain of VBR (variable bit rate) compressed video.
Year
DOI
Venue
1995
10.1109/INFCOM.1995.515943
INFOCOM
Keywords
Field
DocType
empirical distribution,stochastic processes,function space,algorithm,histograms,functional programming,stochastic process,variable bit rate,objective function,autocorrelation,brute force search,nonlinear programming,autocorrelation function,heuristic search,steepest descent,data compression,video compression
Histogram,Parametric equation,Mathematical optimization,Heuristic,Empirical distribution function,Brute-force search,Computer science,Nonlinear programming,Stochastic process,Autocorrelation
Conference
ISSN
ISBN
Citations 
0743-166X
0-8186-6990-X
9
PageRank 
References 
Authors
1.85
3
2
Name
Order
Citations
PageRank
Predrag R. Jelenkovic121929.99
B. Melamed2364122.80