Bayesian Block-Wise Segmentation of Interframe Differences in Video Sequences

Abstract

We present an algorithm for Bayesian estimation of temporally active and inactive spatial regions of video sequences. The algorithm aids in the use of conditional replenishment for video compression in many applications which feature a background/foreground format. For the sake of compatibility with common block-type coders, the binary-valued segmentation is constrained to be constant on square blocks of 8 × 8 or 16 × 16 pixels. Our approach favors connectivity at two levels of scale, with two intended effects. The first is at the pixel level, where a Gibbs distribution is used for the active pixels in the binary field of suprathreshold interframe differences. This increases the value of the likelihood ratio for blocks with spatially contiguous active pixels. The final segmentation also assigns higher probability to patterns of active blocks which are connected, since in general, macroscopic entities are assumed to be many blocks in size. Demonstrations of the advantage of the Bayesian approach are given through several simulations with standard sequences.