An investigation of image compression on NIIRS rating degradation through automated image analysis

Hua-Mei Chen; Erik Blasch; Khanh Pham; Zhonghai Wang; Genshe Chen

doi:10.1117/12.2224631

13 May 2016 An investigation of image compression on NIIRS rating degradation through automated image analysis

Hua-Mei Chen, Erik Blasch, Khanh Pham, Zhonghai Wang, Genshe Chen

Proceedings Volume 9838, Sensors and Systems for Space Applications IX; 983811 (2016) https://doi.org/10.1117/12.2224631
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

The National Imagery Interpretability Rating Scale (NIIRS) is a subjective quantification of static image widely adopted by the Geographic Information System (GIS) community. Efforts have been made to relate NIIRS image quality to sensor parameters using the general image quality equations (GIQE), which make it possible to automatically predict the NIIRS rating of an image through automated image analysis. In this paper, we present an automated procedure to extract line edge profile based on which the NIIRS rating of a given image can be estimated through the GIQEs if the ground sampling distance (GSD) is known. Steps involved include straight edge detection, edge stripes determination, and edge intensity determination, among others. Next, we show how to employ GIQEs to estimate NIIRS degradation without knowing the ground truth GSD and investigate the effects of image compression on the degradation of an image’s NIIRS rating. Specifically, we consider JPEG and JPEG2000 image compression standards. The extensive experimental results demonstrate the effect of image compression on the ground sampling distance and relative edge response, which are the major factors effecting NIIRS rating.

Citation Download Citation

Hua-Mei Chen, Erik Blasch, Khanh Pham, Zhonghai Wang, and Genshe Chen "An investigation of image compression on NIIRS rating degradation through automated image analysis", Proc. SPIE 9838, Sensors and Systems for Space Applications IX, 983811 (13 May 2016); https://doi.org/10.1117/12.2224631

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