On use of image quality metrics for perceptual blur modeling: image/video compression case

Jae H. Cha; Jeffrey T. Olson; Bradley L. Preece; Richard L. Espinola; A. Lynn Abbott

doi:10.1117/1.OE.57.2.023109

27 February 2018 On use of image quality metrics for perceptual blur modeling: image/video compression case

Jae H. Cha, Jeffrey T. Olson, Bradley L. Preece, Richard L. Espinola, A. Lynn Abbott

Author Affiliations +

Optical Engineering, Vol. 57, Issue 2, 023109 (February 2018). https://doi.org/10.1117/1.OE.57.2.023109

Abstract

Linear system theory is employed to make target acquisition performance predictions for electro-optical/infrared imaging systems where the modulation transfer function (MTF) may be imposed from a nonlinear degradation process. Previous research relying on image quality metrics (IQM) methods, which heuristically estimate perceived MTF has supported that an average perceived MTF can be used to model some types of degradation such as image compression. Here, we discuss the validity of the IQM approach by mathematically analyzing the associated heuristics from the perspective of reliability, robustness, and tractability. Experiments with standard images compressed by x.264 encoding suggest that the compression degradation can be estimated by a perceived MTF within boundaries defined by well-behaved curves with marginal error. Our results confirm that the IQM linearizer methodology provides a credible tool for sensor performance modeling.

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Jae H. Cha, Jeffrey T. Olson, Bradley L. Preece, Richard L. Espinola, and A. Lynn Abbott "On use of image quality metrics for perceptual blur modeling: image/video compression case," Optical Engineering 57(2), 023109 (27 February 2018). https://doi.org/10.1117/1.OE.57.2.023109

Received: 14 September 2017; Accepted: 31 January 2018; Published: 27 February 2018

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