The rate-distortion region for multiple descriptions without excess rate
IEEE Transactions on Information Theory
New results in binary multiple descriptions
IEEE Transactions on Information Theory
Elements of information theory
Elements of information theory
Convex Optimization
The worst additive noise under a covariance constraint
IEEE Transactions on Information Theory
On the rate-distortion region for multiple descriptions
IEEE Transactions on Information Theory
Multiple description coding with many channels
IEEE Transactions on Information Theory
n-channel symmetric multiple descriptions - part I: (n, k) source-channel erasure codes
IEEE Transactions on Information Theory
n-channel symmetric multiple descriptions-part II: An achievable rate-distortion region
IEEE Transactions on Information Theory
Vector Gaussian Multiple Description With Individual and Central Receivers
IEEE Transactions on Information Theory
Robust Distributed Source Coding
IEEE Transactions on Information Theory
Approximating the Gaussian multiple description rate region under symmetric distortion constraints
IEEE Transactions on Information Theory
Asymmetric multilevel diversity coding and asymmetric Gaussian multiple descriptions
IEEE Transactions on Information Theory
New coding schemes for the symmetric K -description problem
IEEE Transactions on Information Theory
N-channel asymmetric entropy-constrained multiple-description lattice vector quantization
IEEE Transactions on Information Theory
| Hi-index | 755.08 |
The problem of L multiple descriptions of a stationary and ergodic Gaussian source with two levels of receivers is investigated. Each of the first-level receivers receive (an arbitrary subset) k of the L descriptions, (k L). The second-level receiver receives all L descriptions. All the receivers, both at the first level and the second level, reconstruct the source using the subset of descriptions they receive. The corresponding reconstructions are subject to quadratic distortion constraints. Our main result is the derivation of an outer bound on the sum rate of the descriptions so that the distortion constraints are met. We show that an analog-digital separation architecture involving joint Gaussian vector quantizers and a binning scheme meets this outer bound with equality for several scenarios. These scenarios include the case when the distortion constraints are symmetric and the case for general distortion constraints with k = 2 and L = 3.