Sphere-packings, lattices, and groups
Sphere-packings, lattices, and groups
Discrete-time signal processing
Discrete-time signal processing
Linear Prediction of Speech
Multiple Description Decoding of Overcomplete Expansions Using Projections onto Convex Sets
DCC '99 Proceedings of the Conference on Data Compression
Quantized Oversampled Filter Banks with Erasures
DCC '01 Proceedings of the Data Compression Conference
Quantization noise shaping on arbitrary frame expansions
EURASIP Journal on Applied Signal Processing
Noise-Shaped Predictive Coding for Multiple Descriptions of a Colored Gaussian Source
DCC '08 Proceedings of the Data Compression Conference
IEEE Transactions on Information Theory
Information rates of pre/post-filtered dithered quantizers
IEEE Transactions on Information Theory
Gaussian codes and Shannon bounds for multiple descriptions
IEEE Transactions on Information Theory
Noise reduction in oversampled filter banks using predictive quantization
IEEE Transactions on Information Theory
Multiple-description vector quantization with lattice codebooks: design and analysis
IEEE Transactions on Information Theory
Asymmetric multiple description lattice vector quantizers
IEEE Transactions on Information Theory
Dithered lattice-based quantizers for multiple descriptions
IEEE Transactions on Information Theory
Nested linear/lattice codes for structured multiterminal binning
IEEE Transactions on Information Theory
Filter bank frame expansions with erasures
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
Generalized coset codes for distributed binning
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
Successive Coding in Multiuser Information Theory
IEEE Transactions on Information Theory
Achieving the Gaussian Rate–Distortion Function by Prediction
IEEE Transactions on Information Theory
N-channel asymmetric entropy-constrained multiple-description lattice vector quantization
IEEE Transactions on Information Theory
| Hi-index | 754.90 |
We address the connection between the multiple-description (MD) problem and Delta-Sigma quantization. The inherent redundancy due to oversampling in Delta-Sigma quantization, and the simple linear-additive noise model resulting from dithered lattice quantization, allow us to construct a symmetric and time-invariant MD coding scheme. We show that the use of a noise-shaping filter makes it possible to trade off central distortion for side distortion. Asymptotically, as the dimension of the lattice vector quantizer and order of the noise-shaping filter approach infinity, the entropy rate of the dithered Delta-Sigma quantization scheme approaches the symmetric two-channel MD rate-distortion function for a memoryless Gaussian source and mean square error (MSE) fidelity criterion, at any side-to-central distortion ratio and any resolution. In the optimal scheme, the infinite-order noise-shaping filter must be minimum phase and have a piecewise flat power spectrum with a single jump discontinuity. An important advantage of the proposed design is that it is symmetric in rate and distortion by construction, so the coding rates of the descriptions are identical and there is therefore no need for source splitting.