Tailoring image compression to mission needs: Predicting NIIRS loss due to image compression

Hua-mei Chen; Zhonghai Wang; Genshe Chen; John M. Irvine; Erik Blasch; James Nagy

doi:10.1117/12.2305950

21 May 2018 Tailoring image compression to mission needs: Predicting NIIRS loss due to image compression

Hua-mei Chen, Zhonghai Wang, Genshe Chen, John M. Irvine, Erik Blasch, James Nagy

Proceedings Volume 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII; 1064505 (2018) https://doi.org/10.1117/12.2305950
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Transmission and analysis of imagery for law enforcement and military missions is often constrained by the capacity of available communications channels. Nevertheless, achieving success in operational missions requires acquisition and analysis of imagery that satisfies specific interpretability requirements. By expressing these requirements in terms of the National Imagery Interpretability Ratings Scale (NIIRS), we have developed a method for predicting the NIIRS loss associated with various methods and levels of imagery compression. Our method, known as the Compression Degradation Image Function Index (CoDIFI) framework automatically predicts the NIIRS degradation associated with the specific image compression method and level of compression. In this paper, we first review NIIRS and methods for predicting it followed by the presentation of the CoDIFI framework and we put our emphasis on the results of the empirical validation experiments. By leveraging CoDIFI in operational settings, our goal is to ensure mission success in terms of the NIIRS level of imagery data delivered to users, while optimizing the use of scarce data transmission capacity.

Conference Presentation

Citation Download Citation

Hua-mei Chen, Zhonghai Wang, Genshe Chen, John M. Irvine, Erik Blasch, and James Nagy "Tailoring image compression to mission needs: Predicting NIIRS loss due to image compression", Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 1064505 (21 May 2018); https://doi.org/10.1117/12.2305950

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