Analysis of Optimal Filter Banks for Multiple Description Coding
DCC '00 Proceedings of the Conference on Data Compression
Quantized Oversampled Filter Banks with Erasures
DCC '01 Proceedings of the Data Compression Conference
Multiple description video coding by coefficients ordering and interpolation
MobiMedia '06 Proceedings of the 2nd international conference on Mobile multimedia communications
Multiple Description Scalable Coding for Video Transmission over Unreliable Networks
SAMOS '09 Proceedings of the 9th International Workshop on Embedded Computer Systems: Architectures, Modeling, and Simulation
Filter banks for prediction-compensated multiple description coding
IEEE Transactions on Signal Processing
Multiple description coding for stationary Gaussian sources
IEEE Transactions on Information Theory
Multiple description coding with side information: practical scheme and iterative decoding
EURASIP Journal on Advances in Signal Processing
Multiple description coding for SNR scalable video transmission over unreliable networks
Multimedia Tools and Applications
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Multiple description coding refers to the encoding of an information source into multiple bit streams, in a manner that each bit stream independently represents a “coarse” description of the source, while multiple descriptions jointly convey a higher fidelity source representation. Thus multiple description coding allows enhanced resilience to loss of individual descriptions, and can be viewed as a form of source-based diversity coding. This paper addresses the design of subband filter banks to optimize the rate-distortion performance of multiple description subband coding. The problem is formulated in a Lagrangian optimization framework, and is solved directly in the frequency domain to produce the optimal filter spectral responses. By varying the Lagrangian parameter, our design obtains all points on the redundancy-distortion curve for Gaussian wide-sense stationary input processes. We consider the cases of both orthonormal and more general biorthogonal filter banks. We also analyze the connections between these two solutions, and quantify the gap in their optimal performance. Application of the proposed methods to the popular first-order autoregressive (AR(1)) model yields very interesting and insightful results. In the two extreme cases of maximum and minimum redundancy, our solutions degenerate to previously known results. Finally, comparisons with popularly deployed filter banks reveal large gaps from the theoretical performance bound