Self-Organizing Maps
Generalized Multiple Description Vector Quantization
DCC '99 Proceedings of the Conference on Data Compression
Optimal Index Assignment for Multiple Description Lattice Vector Quantization
DCC '06 Proceedings of the Data Compression Conference
Analysis of K-Channel Multiple Description Quantization
DCC '09 Proceedings of the 2009 Data Compression Conference
M-description lattice vector quantization: index assignment and analysis
IEEE Transactions on Signal Processing
An improvement to multiple description transform coding
IEEE Transactions on Signal Processing
Entropy-constrained index assignments for multiple description quantizers
IEEE Transactions on Signal Processing
LSP-based multiple-description coding for real-time low bit-rate voice over IP
IEEE Transactions on Multimedia
Multiple-description vector quantization with lattice codebooks: design and analysis
IEEE Transactions on Information Theory
Achievable rates for multiple descriptions
IEEE Transactions on Information Theory
Multiple description quantization by deterministic annealing
IEEE Transactions on Information Theory
Design of multiple description scalar quantizers
IEEE Transactions on Information Theory
Generalized-cost-measure-based address-predictive vector quantization
IEEE Transactions on Image Processing
Adaptively post-encoding multiple description video coding
Neurocomputing
Essentials of the self-organizing map
Neural Networks
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Multiple description coding is an appealing tool to guarantee graceful signal degradation in the presence of unreliable channels. While the principles of multiple description scalar quantization are well-understood and solid guidelines exist to design effective systems, the same does not hold for vector quantization, especially at low bit-rates, where burdensome and unsatisfactory design techniques discourage its use altogether in applications. In this work we use the self-organizing maps to design multiple description VQ codebooks. The proposed algorithm is flexible, fast and effective: it deals easily with a large variety of situations, including the case of more than two descriptions, with a computational complexity that remains fully affordable even for large codebooks, and a performance comparable to that of reference techniques. A thorough experimental analysis, conducted in a wide range of operating conditions, proves the proposed technique to perform on par with well-known reference methods based on greedy optimization, but with a much lower computational burden. In addition, the resulting codebook can be itself optimized, thus providing even better performance. All experiments are fully reproducible, with all software and data available online for the interested researchers.