2 research outputs found
Asymmetric Gaussian Multiple Descriptions and Asymmetric Multilevel Diversity Coding
We consider multiple description source coding problem with Gaussian source and mean squared error, for descriptions. We obtain an outer bound for the rate region of the problem. We also derive an inner bound for the problem based on successively refineability of the Gaussian source and multi-level diversity coding. Our gap analysis shows that the difference between two bounds is less than bits, in the worst case
n-Channel Asymmetric Entropy-Constrained Multiple-Description Lattice Vector Quantization
This paper is about the design and analysis of an index-assignment (IA) based
multiple-description coding scheme for the n-channel asymmetric case. We use
entropy constrained lattice vector quantization and restrict attention to
simple reconstruction functions, which are given by the inverse IA function
when all descriptions are received or otherwise by a weighted average of the
received descriptions. We consider smooth sources with finite differential
entropy rate and MSE fidelity criterion. As in previous designs, our
construction is based on nested lattices which are combined through a single IA
function. The results are exact under high-resolution conditions and
asymptotically as the nesting ratios of the lattices approach infinity. For any
n, the design is asymptotically optimal within the class of IA-based schemes.
Moreover, in the case of two descriptions and finite lattice vector dimensions
greater than one, the performance is strictly better than that of existing
designs. In the case of three descriptions, we show that in the limit of large
lattice vector dimensions, points on the inner bound of Pradhan et al. can be
achieved. Furthermore, for three descriptions and finite lattice vector
dimensions, we show that the IA-based approach yields, in the symmetric case, a
smaller rate loss than the recently proposed source-splitting approach.Comment: 49 pages, 4 figures. Accepted for publication in IEEE Transactions on
Information Theory, 201