Energy-efficient image compression for resource-constrained platforms

Authors:
Dong-U Lee;Hyungjin Kim;Mohammad Rahimi;Deborah Estrin;John D. Villasenor
Affiliations:
Mojix, Inc. and Electrical Engineering Department, University of California, Los Angeles, CA;Maxlinear, Inc., Carlsbad and Electrical Engineering Department, University of California, Los Angeles, CA;Nokia Research Center, Palo Alto and Center of Embedded Networked Sensing, University of California, Los Angeles, CA;Computer Science and Electrical Engineering Departments, University of California, Los Angeles, CA;Electrical Engineering Department, University of California, Los Angeles, CA
Venue:
IEEE Transactions on Image Processing
Year:
2009

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Abstract

One of the most important goals of current and future sensor networks is energy-efficient communication of images. This paper presents a quantitative comparison between the energy costs associated with 1) direct transmission of uncompressed images and 2) sensor platform-based JPEG compression followed by transmission of the compressed image data. JPEG compression computations are mapped onto various resource-constrained platforms using a design environment that allows computation using the minimum integer and fractional bit-widths needed in view of other approximations inherent in the compression process and choice of image quality parameters. Advanced applications of JPEG, such as region of interest coding and successive/progressive transmission, are also examined. Detailed experimental results examining the tradeoffs in processor resources, processing/transmission time, bandwidth utilization, image quality, and overall energy consumption are presented.