Prediction of compression-induced image interpretability degradation

Erik Blasch; Hua-Mei Chen; John M. Irvine; Zhonghai Wang; Genshe Chen; James Nagy; Stephen Scott

doi:10.1117/1.OE.57.4.043108

20 April 2018 Prediction of compression-induced image interpretability degradation

Erik Blasch, Hua-Mei Chen, John M. Irvine, Zhonghai Wang, Genshe Chen, James Nagy, Stephen Scott

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Funded by: Air Force Research Laboratory

Optical Engineering, Vol. 57, Issue 4, 043108 (April 2018). https://doi.org/10.1117/1.OE.57.4.043108

Abstract

Image compression is an important component in modern imaging systems as the volume of the raw data collected is increasing. To reduce the volume of data while collecting imagery useful for analysis, choosing the appropriate image compression method is desired. Lossless compression is able to preserve all the information, but it has limited reduction power. On the other hand, lossy compression, which may result in very high compression ratios, suffers from information loss. We model the compression-induced information loss in terms of the National Imagery Interpretability Rating Scale or NIIRS. NIIRS is a user-based quantification of image interpretability widely adopted by the Geographic Information System community. Specifically, we present the Compression Degradation Image Function Index (CoDIFI) framework that predicts the NIIRS degradation (i.e., a decrease of NIIRS level) for a given compression setting. The CoDIFI-NIIRS framework enables a user to broker the maximum compression setting while maintaining a specified NIIRS rating.

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Erik Blasch, Hua-Mei Chen, John M. Irvine, Zhonghai Wang, Genshe Chen, James Nagy, and Stephen Scott "Prediction of compression-induced image interpretability degradation," Optical Engineering 57(4), 043108 (20 April 2018). https://doi.org/10.1117/1.OE.57.4.043108

Received: 7 November 2017; Accepted: 14 March 2018; Published: 20 April 2018

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