Reducing the Computational Complexity of a Map Post-Processing Algorithm for Video Sequences

Maximum a posteriori (MAP) filtering using the HuberMarkov random field (HMRF) image model has been shown in the past to be an effective method of reducing compression artifacts in images [2]. Unfortunately, this MAP formulation requires iterative techniques for the solution of a constrained optimization problem. In the past, these iterative techniques have been computationally intensive, making the filter infeasible in situations where it is desired to filter images (or video frames) quickly. This paper introduces two methods for reducing the computational requirements of the constrained optimization, as well as theoretical and experimental justifications for using them.