Title :
Multiple description lattice vector quantization: variations and extensions
Author :
J.A. Kelner;V.K. Goyal;J. Kovacevic
Author_Institution :
Harvard Univ., Cambridge, MA, USA
Abstract :
Multiple description lattice vector quantization (MDLVQ) is a technique for two-channel multiple description coding. We observe that MDLVQ, in the form introduced by Servetto et al. (1999), is inherently optimized for the central decoder; i.e., for zero probability of a lost description. With a nonzero probability of description loss, performance is improved by modifying the encoding rule (using nearest neighbors with respect to "multiple description distance") and by perturbing the lattice codebook. The perturbation maintains many symmetries and hence does not significantly affect encoding or decoding complexity. An extension to more than two descriptions with attractive decoding properties is outlined.
Keywords :
"Lattices","Vector quantization","Decoding","Encoding","Mathematics","Performance loss","Nearest neighbor searches","Electrical capacitance tomography","Source coding","Redundancy"
Conference_Titel :
Data Compression Conference, 2000. Proceedings. DCC 2000
Print_ISBN :
0-7695-0592-9
DOI :
10.1109/DCC.2000.838188