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Proceedings Paper

Dynamically prioritized progressive transmission
Author(s): Ronald Blanford
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Paper Abstract

Retrieval of image data from a centralized database may be subject to bandwidth limitations, whether due to a low-bandwidth communications link or to contention from simultaneous accesses over a high-bandwidth link. Progressive transmission can alleviate this problem by encoding image data so that any prefix of the data stream approximates the complete image at a coarse level of resolution. The longer the prefix, the finer the resolution. In many cases, as little at 1 percent of the image data may be sufficient to decide whether to discard the image, to permit the retrieval to continue, or to restrict retrieval to a subsection of the image. Our approach treats resolution not as a fixed attribute of the image, but rather as a resource which may be allocated to portions of the image at the direction of a user-specified priority function. The default priority function minimizes error by allocating more resolution to regions of high variance. The user may also point to regions of interest requesting priority transmission. More advanced target recognition strategies may be incorporated at the user's discretion. Multispectral imagery is supported. The user engineering implications are profounded. There is immediate response to a query that might otherwise take minutes to complete. The data is transmitted in small increments so that no single user dominates the communications bandwidth. The user-directed improvement means that bandwidth is focused on interesting information. The user may continue working with the first coarse approximations while further image data is still arriving. The algorithm has been implemented in C on Sun, Silicon Graphics, and NeXT workstations, and in Lisp on a Symbolics. Transmission speeds reach as high as 60,000 baud using a Sparc or 68040 processor when storing data to memory; somewhat less if also updating a graphical display. The memory requirements are roughly five bytes per image pixel. Both computational and memory costs may be reduced by increasing the time between priority computations. Progressive transmission improves the performance of lossless LZW or Huffman compression. If exact reconstruction of the image is not needed, the transmitted values may be quantized to achieve further compression. Our experience shows the technique to be flexible enough to support a variety of situations.

Paper Details

Date Published: 1 April 1992
PDF: 1 pages
Proc. SPIE 1623, The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges, (1 April 1992); doi: 10.1117/12.58057
Show Author Affiliations
Ronald Blanford, TRW, Inc. (United States)


Published in SPIE Proceedings Vol. 1623:
The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges

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