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M -Description Lattice Vector Quantization: Index Assignment and Analysis

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2 Author(s)
Minglei Liu ; Sch. of Commun. & Inf. Eng., Chongqing Univ. of Posts & Telecommun., Chongqing ; Ce Zhu

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.

Published in:

IEEE Transactions on Signal Processing  (Volume:57 ,  Issue: 6 )