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. https://resolver.caltech.edu/CaltechAUTHORS:MARieeetit06
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Abstract
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.
Item Type: | Article | ||||||
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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 | ||||||
DOI: | 10.1109/TIT.2006.871610 | ||||||
Record Number: | CaltechAUTHORS:MARieeetit06 | ||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:MARieeetit06 | ||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||
ID Code: | 4001 | ||||||
Collection: | CaltechAUTHORS | ||||||
Deposited By: | Archive Administrator | ||||||
Deposited On: | 24 Jul 2006 | ||||||
Last Modified: | 08 Nov 2021 20:14 |
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