Image compression quality metrics

Harold H. Szu; Charles C. Hsu; Joseph Landa; Terry L. Jones; Barbara L. O'Kane; John Desomond O'Connor; Romain Murenzi; Mark J. T. Smith

doi:10.1117/12.271768

3 April 1997 Image compression quality metrics

Harold H. Szu, Charles C. Hsu, Joseph Landa, Terry L. Jones, Barbara L. O'Kane, John Desomond O'Connor, Romain Murenzi, Mark J. T. Smith

Author Affiliations +

Proceedings Volume 3078, Wavelet Applications IV; (1997) https://doi.org/10.1117/12.271768
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

Battlefield reconnaissance through tactical surveillance video systems requires transmission of images through a limited bandwidth and capacity to achieve aided target recognition (ATR), of which some lossy compression is indispensable. Based on available resolution, ATR can have three functionality goals: (1) detection of a target, (2) recognition of target classes, and (3) identification of individual target membership. Thus, it is desirable to build an intelligent lookup table which maps a specific ATR goal into an appropriate image compression. Such a table may be built implicitly be employing the exemplar training procedure of artificial neutral networks. In order to illustrate this concept, we will introduce a computational metric called feature persistence measure, useful for x-ray luggage inspections, and further generalized here to capture human performance in a tactical imaging scenario.

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Harold H. Szu, Charles C. Hsu, Joseph Landa, Terry L. Jones, Barbara L. O'Kane, John Desomond O'Connor, Romain Murenzi, and Mark J. T. Smith "Image compression quality metrics", Proc. SPIE 3078, Wavelet Applications IV, (3 April 1997); https://doi.org/10.1117/12.271768

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