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Proceedings Paper

Computational scalability of large size image dissemination
Author(s): Rob Kooper; Peter Bajcsy
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Paper Abstract

We have investigated the computational scalability of image pyramid building needed for dissemination of very large image data. The sources of large images include high resolution microscopes and telescopes, remote sensing and airborne imaging, and high resolution scanners. The term 'large' is understood from a user perspective which means either larger than a display size or larger than a memory/disk to hold the image data. The application drivers for our work are digitization projects such as the Lincoln Papers project (each image scan is about 100-150MB or about 5000x8000 pixels with the total number to be around 200,000) and the UIUC library scanning project for historical maps from 17th and 18th century (smaller number but larger images). The goal of our work is understand computational scalability of the web-based dissemination using image pyramids for these large image scans, as well as the preservation aspects of the data. We report our computational benchmarks for (a) building image pyramids to be disseminated using the Microsoft Seadragon library, (b) a computation execution approach using hyper-threading to generate image pyramids and to utilize the underlying hardware, and (c) an image pyramid preservation approach using various hard drive configurations of Redundant Array of Independent Disks (RAID) drives for input/output operations. The benchmarks are obtained with a map (334.61 MB, JPEG format, 17591x15014 pixels). The discussion combines the speed and preservation objectives.

Paper Details

Date Published: 25 January 2011
PDF: 7 pages
Proc. SPIE 7872, Parallel Processing for Imaging Applications, 78720N (25 January 2011); doi: 10.1117/12.872834
Show Author Affiliations
Rob Kooper, Univ. of Illinois at Urbana-Champaign (United States)
Peter Bajcsy, Univ. of Illinois at Urbana-Champaign (United States)

Published in SPIE Proceedings Vol. 7872:
Parallel Processing for Imaging Applications
John D. Owens; I-Jong Lin; Yu-Jin Zhang; Giordano B. Beretta, Editor(s)

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