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Proceedings Paper

Visually lossless compression of digitized radiographs based on contrast sensitivity and visual masking
Author(s): Damon Michael Chandler; Nathan L. Dykes; Sheila S. Hemami
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Paper Abstract

A visually lossless compression algorithm for digitized radiographs, which predicts the maximum contrast that wavelet subband quantization distortions can exhibit in the reconstructed image such that the distortions are visually undetectable, is presented. Via a psychophysical experiment, contrast thresholds were measured for the detection of 1.15-18.4 cycles/degree wavelet subband quantization distortions in five digitized radiographs; results indicate that digitized radiographs impose image- and frequency-selective effects on detection. A quantization algorithm is presented which predicts the thresholds for individual images based on a model of visual masking. When incorporated into JPEG-2000 and applied to a suite of images, results indicate that digitized radiographs can be compressed in a visually lossless manner at an average compression ratio of 6.25:1, with some images requiring visually lossless ratios as low as 4:1 and as great as 9:1. The proposed algorithm thus yields images that require the minimum bit-rate such that the reconstructed images are visually indistinguishable from the original images. The primary utility of the proposed algorithm is its ability to provide image-adaptive visually lossless compression, thereby avoiding overly conservative or overly aggressive compression.

Paper Details

Date Published: 6 April 2005
PDF: 14 pages
Proc. SPIE 5749, Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment, (6 April 2005); doi: 10.1117/12.595614
Show Author Affiliations
Damon Michael Chandler, Cornell Univ. (United States)
Nathan L. Dykes, Cornell Univ. (United States)
Sheila S. Hemami, Cornell Univ. (United States)

Published in SPIE Proceedings Vol. 5749:
Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment
Miguel P. Eckstein; Yulei Jiang, Editor(s)

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