Stochastic modeling and entropy constrained estimation of motion from image sequences

Abstract

We consider the problem of coding video signals using motion compensation and a forward coded dense motion field. First, we develop a motion estimation technique that yields dense estimates suitable for the coding application; next, we develop a prototype of a video coder, which we use to verify that high coding performance is attainable within our framework. To find our sought motion estimates, we assume motion in an observed image sequence to be a stochastic process, modeled as a Markov random field (MRF). The standard maximum a posteriori (MAP) estimation problem with MRF priors is formulated as a constrained optimization problem (where the constraint is on the entropy of the sought estimate), but then transformed into a classical MAP estimation problem, and solved using standard techniques. A key advantage of the constrained formalization is that, in the process of transforming it back to the classical framework, parameters which in the classical framework are left unspecified (and often tweaked in an experimental stage) become now uniquely determined by the introduced entropy constraint. To verify that our motion estimates are indeed useful for coding, we compare the performance of a prototype video coder with that of an equivalent coder based on block-matching motion estimates. Experimental results reveal, for various types of video signals and at various rates, that: (a) in terms of PSNR, our system equals or improves upon the performance of full search block matching; and (b) in terms of visual quality our improvements are significant, since our images are completely free of blocking artifacts.