DocumentCode
2707598
Title
On global optimality of gradient descent algorithms for fixed-rate scalar multiple description quantizer design
Author
Dumitrescu, Sorina ; Wu, Xiaolin
Author_Institution
Dept. of Electr. & Comput. Eng., McMaster Univ., Hamilton, Ont., Canada
fYear
2005
fDate
29-31 March 2005
Firstpage
388
Lastpage
397
Abstract
We prove that Trushkin´s (1982) sufficient conditions for the global optimality of a locally optimal fixed-rate scalar quantizer also ensure the global optimality of a locally optimal fixed-rate multiple description scalar quantizer of convex codecells, with respect to a fixed index assignment. This result also holds for the fixed-rate multiresolution scalar quantizer of convex codecells. As a consequence the well-known log-concave pdf condition can be extended to the multiple description and multiresolution case.
Keywords
codes; gradient methods; optimisation; probability; vector quantisation; convex codecells; fixed index assignment; fixed-rate quantizer design; global optimality; gradient descent algorithms; log-concave pdf condition; multiresolution scalar quantizer; scalar multiple description quantizer; Algorithm design and analysis; Data compression; Probability density function; Probability distribution; Quantization; Random variables; Sufficient conditions;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Compression Conference, 2005. Proceedings. DCC 2005
ISSN
1068-0314
Print_ISBN
0-7695-2309-9
Type
conf
DOI
10.1109/DCC.2005.60
Filename
1402200
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