Sphere-packings, lattices, and groups
Sphere-packings, lattices, and groups
A faster strongly polynomial minimum cost flow algorithm
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Efficient Algorithms for the Hitchcock Transportation Problem
SIAM Journal on Computing
Concrete Math
Multiple Description Lattice 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
Introduction to Operations Research and Revised CD-ROM 8
Introduction to Operations Research and Revised CD-ROM 8
Multiple-Description Coding by Dithered Delta-Sigma Quantization
DCC '07 Proceedings of the 2007 Data Compression Conference
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
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
Multiple description wavelet based image coding
IEEE Transactions on Image Processing
Optimized Multiple Description Lattice Vector Quantization for Wavelet Image Coding
IEEE Transactions on Circuits and Systems for Video Technology
Multiple description coded video streaming in peer-to-peer networks
Image Communication
| Hi-index | 35.68 |
In this paper, we investigate the design of symmetric entropy-constrained multiple description lattice vector quantization (MDLVQ), more specifically, MDLVQ index assignment. We consider a fine lattice containing clean similar sublattices with S-similarity. Due to the S-similarity of the sublattices, an M-fraction lattice can be used to regularly partition the fine lattice with smaller Voronoi cells than a sublattice does. With the partition, the MDLVQ index assignment design can be translated into a transportation problem in operations research. Both greedy and general algorithms are developed to pursue optimality of the index assignment. Under high-resolution assumption, we compare the proposed schemes with other relevant techniques in terms of optimality and complexity. Following our index assignment design, we also obtain an asymptotical close-form expression of k-description side distortion. Simulation results on coding different sources of Gaussian, speech and image are presented to validate the effectiveness of the proposed schemes.