Balanced multiple description coding in image communications

Latest developments in video coding technology on one side, and a continuous growth in size and bandwidth in lossy networks on the other side, have undoubtedly created a whole new world of multimedia communications. However, nowadays networks, which are best-effort in nature, are unable to guarantee the stringent delay constraints and bandwidth requirements imposed by many of these applications. Therefore, the main challenge remains to find efficient coding techniques which do not require retransmission and which can ensure a good reconstruction quality if pieces of information are missing. Multiple description coding (MDC) offers an elegant and competitive solution for data transmission over lossy packet-based networks, with a graceful degradation in quality as losses increase. In MDC, two or more representations of a source are generated in such a way that an acceptable quality is ensured even if only one description is received, while this quality further improves as more of them are combined. In this thesis, we address some important issues in MDC. One of them is how to generate an arbitrary number of descriptions, as it has been suggested by many researchers that having a scheme which adapts the number of descriptions to different lossy scenarios can be of great benefit. Another interesting problem is how to combine the principles of multiple description coding and increasingly popular redundant signal expansions, since they represent a natural candidate for MDC. Finally, our goal is to address the problem of designing a simple and efficient multiple description video coding scheme, which utilizes error resilience tools o ered by the latest video coding standard, H.264/AVC. We first address the generation of an arbitrary number of descriptions with the multiple description scalar quantization technique. Unlike the existing solutions whose complexity drastically increases when the number of descriptions augments, our solution remains very simple and easily extendable to any number of descriptions. We show how the tradeoff between distortions can be easily controlled with very few parameters in our scheme. Finally, given the probability of losing a description and the total bitrate, we find the optimal number of descriptions which minimizes the average distortion, taken as a sum of distortions weighted by the corresponding probabilities. Next, we address the multiple description coding problem with redundant dictionaries of functions, called the atoms. Such dictionaries contain inherent redundancy, which can be efficiently exploited for MDC purpose. To do so, we cluster similar atoms together and represent each group by the molecules, taken as a weighted sum of the atoms in its clusters. Once a molecule is chosen as a good candidate in the signal representation, its children are distributed to different descriptions. To generate a description, we project a signal onto the sets of chosen atoms. This further gives us the sets of coefficients, wh