Measurements vs. Bits: Compressed Sensing meets Information Theory

Files in this item

Files Size Format View
Sar2006Sep5Measuremen.PDF 130.1Kb application/pdf Thumbnail
Sar2006Sep5Measuremen.PPT 720.3Kb application/ View/Open

Show full item record

Item Metadata

Title: Measurements vs. Bits: Compressed Sensing meets Information Theory
Author: Sarvotham, Shriram; Baron, Dror; Baraniuk, Richard G.
Type: Conference paper
Keywords: Compressed sensing; information theory
Citation: S. Sarvotham, D. Baron and R. G. Baraniuk, "Measurements vs. Bits: Compressed Sensing meets Information Theory," 2006.
Abstract: Compressed sensing is a new framework for acquiring sparse signals based on the revelation that a small number of linear projections (measurements) of the signal contain enough information for its reconstruction. The foundation of Compressed sensing is built on the availability of noise-free measurements. However, measurement noise is unavoidable in analog systems and must be accounted for. We demonstrate that measurement noise is the crucial factor that dictates the number of measurements needed for reconstruction. To establish this result, we evaluate the information contained in the measurements by viewing the measurement system as an information theoretic channel. Combining the capacity of this channel with the rate-distortion function of the sparse signal, we lower bound the rate-distortion performance of a compressed sensing system. Our approach concisely captures the effect of measurement noise on the performance limits of signal reconstruction, thus enabling to benchmark the performance of specific reconstruction algorithms.
Date Published: 2006-09-01

This item appears in the following Collection(s)

  • ECE Publications [1053 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.