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Proceedings Paper

Task-specific image quality metrics for lossy compression of FLIR images
Author(s): Olga Kosheleva; Carlos Mendoza; Sergio D. Cabrera
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Paper Abstract

In this paper we evaluate two lossy compression methods, JPEG and SPIHT, using a metric computation system which evaluates the distortion effects of lossy compression as it affects the task of target segmentation with morphological filtering. Using Forward Looking Infrared Radar images to develop the approach, our goal is to contrast the results of the identical tasks with and without compression using traditional metrics, local/target area traditional metrics, and binary metrics applied to the component representing the selected target mask. Thus, our metrics are specifically targeted to measure the degree of invariance of the processing to the presence of an initial compression- decompression step. The two segmentation methods used are the fuzzy c-means and the median cut. The results indicate that even though SPIHT is better than JPEG, this is not always the case in terms of the binary metrics. In addition, the fuzzy c-means segmentation method is better than the median cut in most cases. Another interesting effect observed is that small changes in traditional metrics can sometimes lead to a drastic change in the task-specific metrics.

Paper Details

Date Published: 27 July 1999
PDF: 12 pages
Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999); doi: 10.1117/12.357180
Show Author Affiliations
Olga Kosheleva, Univ. of Texas/El Paso (United States)
Carlos Mendoza, Univ. of Texas/El Paso (United States)
Sergio D. Cabrera, Univ. of Texas/El Paso (United States)

Published in SPIE Proceedings Vol. 3720:
Signal Processing, Sensor Fusion, and Target Recognition VIII
Ivan Kadar, Editor(s)

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