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Low-resolution scalar quantization for Gaussian sources and squared error

Marco, Daniel and Neuhoff, David L. (2006) Low-resolution scalar quantization for Gaussian sources and squared error. IEEE Transactions on Information Theory, 52 (4). pp. 1689-1697. ISSN 0018-9448. doi:10.1109/TIT.2006.871610.

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This correspondence analyzes the low-resolution performance of entropy-constrained scalar quantization. It focuses mostly on Gaussian sources, for which it is shown that for both binary quantizers and infinite-level uniform threshold quantizers, as D approaches the source variance /spl sigma//sup 2/, the least entropy of such quantizers with mean-squared error D or less approaches zero with slope -log/sub 2/e/2/spl sigma//sup 2/. As the Shannon rate-distortion function approaches zero with the same slope, this shows that in the low-resolution region, scalar quantization with entropy coding is asymptotically as good as any coding technique.

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Additional Information:© Copyright 2006 IEEE. Reprinted with permission. Manuscript received June 1, 2004; revised December 27, 2005. [Posted online: 2006-04-03] This work was supported by the National Science Foundation under Grant ANI-0112801. This material in this correspondence was presented at the IEEE International Symposium on Information Theory, Chicago, IL, June/July 2004. Communicated by M. Effros, Associate Editor for Source Coding.
Subject Keywords:Entropy constrained quantization; Gaussian; low rate; low resolution; scalar quantization; squared error
Issue or Number:4
Record Number:CaltechAUTHORS:MARieeetit06
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:4001
Deposited By: Archive Administrator
Deposited On:24 Jul 2006
Last Modified:08 Nov 2021 20:14

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