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Proceedings Paper

Adaptive storage and retrieval of large compressed images
Author(s): John R. Smith; Vittorio Castelli; Chung-Sheng Li
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Paper Abstract

Enabling the efficient storage, access and retrieval of large volumes of multidimensional data is one of the important emerging problems in databases. We present a framework for adaptively storing, accessing, and retrieving large images. The framework uses a space and frequency graph to generate and select image view elements for storing in the database. By adapting to user access patterns, the system selects and stores those view elements that yield the lowest average cost for accessing the multiresolution subregion image views. The system uses a second adaptation strategy to divide computation between server and client in progressive retrieval of image views using view elements. We show that the system speeds-up retrieval for access and retrieval modes, such as drill-down browsing and remote zooming and panning, and minimizes the amount of data transfer over the network.

Paper Details

Date Published: 17 December 1998
PDF: 12 pages
Proc. SPIE 3656, Storage and Retrieval for Image and Video Databases VII, (17 December 1998); doi: 10.1117/12.333866
Show Author Affiliations
John R. Smith, IBM Thomas J. Watson Research Ctr. (United States)
Vittorio Castelli, IBM Thomas J. Watson Research Ctr. (United States)
Chung-Sheng Li, IBM Thomas J. Watson Research Ctr. (United States)


Published in SPIE Proceedings Vol. 3656:
Storage and Retrieval for Image and Video Databases VII
Minerva M. Yeung; Boon-Lock Yeo; Charles A. Bouman, Editor(s)

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