Generalized multiple description coding with correlating transforms

TL;DR: Numerical studies show that this method performs well at low redundancies, as compared to uniform MD scalar quantization, and several transform optimization results are presented for memoryless Gaussian sources.

Abstract: Multiple description (MD) coding is source coding in which several descriptions of the source are produced such that various reconstruction qualities are obtained from different subsets of the descriptions. Unlike multiresolution or layered source coding, there is no hierarchy of descriptions; thus, MD coding is suitable for packet erasure channels or networks without priority provisions. Generalizing work by Orchard, Wang, Vaishampayan and Reibman (see Proc IEEE Int. Conf. Image Processing, vol.I, Santa Barbara, CA, p.608-11, 1997), a transform-based approach is developed for producing M descriptions of an N-tuple source, M/spl les/N. The descriptions are sets of transform coefficients, and the transform coefficients of different descriptions are correlated so that missing coefficients can be estimated. Several transform optimization results are presented for memoryless Gaussian sources, including a complete solution of the N=2, M=2 case with arbitrary weighting of the descriptions. The technique is effective only when independent components of the source have differing variances. Numerical studies show that this method performs well at low redundancies, as compared to uniform MD scalar quantization.