Power-Efficient Cameras Using Natural Image Statistics

Roni Feldman, Yair Weiss, Yonina C. Eldar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Motivated by recent results on compressed sensing cameras we consider cameras that perform an analog linear transformation Φ on the signal, followed by scalar quantization. Specifically we ask: is it better to use compressed sensing (Φ is an under-sampling random matrix) or direct sensing (Φ is the sparsifying basis)? We compare the two approaches using their energy-distortion tradeoffs: assuming most of the energy consumed by such systems is in the ADC and the energy of the quantizer doubles with each bit, which system will give lower distortion for the same energy consumption? We present analytic expressions for the energy-distortion curves for three signal models: signals residing in a known subspace, sparse signals and power-law signals. For all of these models, our analysis shows that direct sensing results in lower distortion for a given energy consumption. We also present simulation results for natural images showing that direct sensing of Haar wavelet coefficients is preferable for these signals. Given the assumptions of our model, direct sensing of Haar wavelets can achieve high quality imaging (PSNR of 40 dB) with 6% the power consumption of standard cameras using 8 bits per channel.

Original languageAmerican English
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
PublisherIEEE Computer Society
Pages945-953
Number of pages9
ISBN (Electronic)9781467388504
DOIs
StatePublished - 16 Dec 2016
Event29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 - Las Vegas, United States
Duration: 26 Jun 20161 Jul 2016

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
Country/TerritoryUnited States
CityLas Vegas
Period26/06/161/07/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

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