Coding-oriented video segmentation inspired by MRF models

This paper presents an approach to the segmentation of video sequences that is inspired by Markov random field (MRF) models and is aimed at region-based video compression. Two goals of the segmentation algorithm are considered: to assure a rate-efficient partitioning of video sequences and to provide regions that are meaningful for human observers ("coding for content"). To address both issues we extend our earlier work; we incorporate a segmentation complexity measure to account for the rate allocated to region shape, we use a robust error criterion to reject outliers in the intensity residual and we incorporate a temporal consistency constraint to assure the continuity of segmentation in time. We demonstrate improvements in the segmentation for real videoconferencing sequences.