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Proceedings Paper

Variable Rate Vector Quantization For Medical Image Compression With Applications To Progressive Transmission
Author(s): Eve A. Riskin; Tom Lookabaugh; Philip A. Chou; Robert M. Gray
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Paper Abstract

In this work, a new technique for variable rate VQ design based on tree structures is applied to medical images. It is an extension of an algorithm for optimal pruning in tree-structured classification and regression due to Breiman, Friedman, Olshen, and Stone [1]. The algorithm finds subtrees of a given tree-structured vector quantizer (TSVQ), each one optimal in that it has the lowest average distortion of all subtrees with the same or lesser average rate [2]. Since the resulting subtrees have variable height, natural variable rate coders result. Image reproduction at 1.5 bits per pixel is excellent and pathology in brain magnetic resonance images can be diagnosed in images at less than 0.5 bit per pixel. Finally, TSVQ is stored in a format convenient for progressive transmission of images.

Paper Details

Date Published: 8 May 1989
PDF: 11 pages
Proc. SPIE 1091, Medical Imaging III: Image Capture and Display, (8 May 1989); doi: 10.1117/12.976445
Show Author Affiliations
Eve A. Riskin, Stanford University (United States)
Tom Lookabaugh, Compression Labs Inc. (United States)
Philip A. Chou, AT&T Bell Laboratories and Stanford University (United States)
Robert M. Gray, Stanford University (United States)

Published in SPIE Proceedings Vol. 1091:
Medical Imaging III: Image Capture and Display
Samuel J. Dwyer; R. Gilbert Jost; Roger H. Schneider, Editor(s)

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