Source Coding Theory
Multiple Description Decoding of Overcomplete Expansions Using Projections onto Convex Sets
DCC '99 Proceedings of the Conference on Data Compression
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Multiple Description Coding for Stationary and Ergodic Sources
DCC '07 Proceedings of the 2007 Data Compression Conference
Noise-Shaped Predictive Coding for Multiple Descriptions of a Colored Gaussian Source
DCC '08 Proceedings of the Data Compression Conference
Vector Gaussian multiple description with two levels of receivers
IEEE Transactions on Information Theory
Multiple-description coding by dithered delta-sigma quantization
IEEE Transactions on Information Theory
Approximating the Gaussian multiple description rate region under symmetric distortion constraints
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Asymptotic analysis of multiple description quantizers
IEEE Transactions on Information Theory
Gaussian codes and Shannon bounds for multiple descriptions
IEEE Transactions on Information Theory
The analysis and design of windowed Fourier frame based multiple description source coding schemes
IEEE Transactions on Information Theory
Multiple-description vector quantization with lattice codebooks: design and analysis
IEEE Transactions on Information Theory
Generalized multiple description coding with correlating transforms
IEEE Transactions on Information Theory
Asymmetric multiple description lattice vector quantizers
IEEE Transactions on Information Theory
Nested linear/lattice codes for structured multiterminal binning
IEEE Transactions on Information Theory
Index assignment for multichannel communication under failure
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
IEEE Transactions on Information Theory
Universal multiple description scalar quantization: analysis and design
IEEE Transactions on Information Theory
Achieving 1/2 log (1+SNR) on the AWGN channel with lattice encoding and decoding
IEEE Transactions on Information Theory
n-channel symmetric multiple descriptions-part II: An achievable rate-distortion region
IEEE Transactions on Information Theory
n-channel entropy-constrained multiple-description lattice vector quantization
IEEE Transactions on Information Theory
Multiple Description Quantization Via Gram–Schmidt Orthogonalization
IEEE Transactions on Information Theory
Hi-index | 754.84 |
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.