Automated TES modeling of compressed video

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.