Adaptive storage and retrieval of large compressed images

John R. Smith; Vittorio Castelli; Chung-Sheng Li

doi:10.1117/12.333866

17 December 1998 Adaptive storage and retrieval of large compressed images

John R. Smith, Vittorio Castelli, Chung-Sheng Li

Proceedings Volume 3656, Storage and Retrieval for Image and Video Databases VII; (1998) https://doi.org/10.1117/12.333866
Event: Electronic Imaging '99, 1999, San Jose, CA, United States

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.

Citation Download Citation

John R. Smith, Vittorio Castelli, and Chung-Sheng Li "Adaptive storage and retrieval of large compressed images", Proc. SPIE 3656, Storage and Retrieval for Image and Video Databases VII, (17 December 1998); https://doi.org/10.1117/12.333866

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