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Multiple description coding offers an elegant and competitive solution for data transmission over lossy packet-based  networks, with a graceful degradation in quality as losses  increase. In the same time, coding techniques based on redundant  transforms give a very promising alternative for the generation of  multiple descriptions, mainly due to redundancy inherently given  by a transform, which offers intrinsic resiliency in case of loss.  In this paper, we show how partitioning of a generic redundant  dictionary can be used to obtain an arbitrary number of multiple  complementary, yet correlated, descriptions. The most significant  terms in the signal representation are drawn from the partitions  that better approximate the signal, and split to different  descriptions, while the less important ones are alternatively  distributed between the descriptions. As compared to  state-of-the-art solutions, such a strategy allows for a better  central distortion since atoms in different descriptions are not  identical; in the same time, it does not penalize the side  distortions significantly since atoms from the same partition are  likely to be highly correlated. The proposed scheme is applied to  the multiple description coding of digital images, and simulation  results show increased performances compared to state-of-the-art  schemes, both in terms of distortions and robustness to loss rate  variations

Multiple Description Coding with Redundant Expansions and Application to Image Communications