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Proceedings Paper

Perspectives on data compression for estimations from sensors
Author(s): Mark L. Fowler; Mo Chen
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Paper Abstract

Data compression methods have mostly focused on achieving a desired perception quality for multi-media data for a given number of bits. However, there has been interest over the last several decades on compression for communicating data to a remote location where the data is used to compute estimates. This paper traces the perspectives in the research literature for compression-for-estimation. We discuss how these perspectives can all be cast in the following form: the source emits a signal - possibly dependent on some unknown parameter(s), the ith sensor receives the signal and compresses it for transmission to a central processing center where it is used to make the estimate(s). The previous perspectives can be grouped as optimizing compression for the purpose of either (i) estimation of the source signal or (ii) the source parameter. Early results focused on restricting the encoder to being a scalar quantizer that is designed according to some optimization criteria. Later results focused on more general compression structures, although, most of those focus on establishing information theoretic results and bounds. Recent results by the authors use operational rate-distortion methods to develop task-driven compression algorithms that allow trade-offs between the multiple estimation tasks for a given rate.

Paper Details

Date Published: 25 August 2006
PDF: 11 pages
Proc. SPIE 6315, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX, 631505 (25 August 2006); doi: 10.1117/12.683422
Show Author Affiliations
Mark L. Fowler, Binghamton Univ. (United States)
Mo Chen, Binghamton Univ. (United States)

Published in SPIE Proceedings Vol. 6315:
Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX
Gerhard X. Ritter; Mark S. Schmalz; Junior Barrera; Jaakko T. Astola, Editor(s)

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