Abstract
Multiple description coding can be implemented as pre- and postprocessing to all standards for image and video communications, with obvious advantages. This can be achieved by generating two subsets from the original data; a controllable amount of extra redundancy between the descriptions has to be inserted to help the estimation of the possibly lost description from the received one. This redundancy can be in the form of spatial oversampling. In this paper, we propose and develop a mathematical framework for two descriptions pre- and postprocessing methods, which exploit the correlation characteristics of the visual data in order to better implement the multiple description coding paradigm. Simulation results show a noticeable performance improvement of both the proposed methods with respect to state-of-the art algorithms, in terms of both rate/redundancy/distortion tradeoff and computational complexity.