Abstract
Abstract. The General Image Quality Equation (GIQE) is an analytical tool derived by regression modelling that is routinely employed to gauge the interpretability of raw and processed images, computing the most popular quantitative metric to evaluate image quality; the National Image Interpretability Rating Scale (NIIRS). There are three known versions of this equation; GIQE 3, GIQE 4 and GIQE 5, but the last one is scarcely known. The variety of versions, their subtleties, discontinuities and incongruences, generate confusion and problems among users. The first objective of this paper is to identify typical sources of confusion in the use of the GIQE, suggesting novel solutions to the main problems found in its application and presenting the derivation of a continuous form of GIQE 4, denominated GIQE 4C, that provides better correlation with GIQE 3 and GIQE 5. The second objective of this paper is to compare the predictions of GIQE 4C and GIQE 5, regarding the maximum image quality rating that can be achieved by image processing techniques. It is concluded that the transition from GIQE 4 to GIQE 5 is a major paradigm shift in image quality metrics, because it reduces the benefit of image processing techniques and enhances the importance of the raw image and its signal to noise ratio.
Highlights
The National Image Interpretability Rating Scale (NIIRS) was developed in the 1970 ́s under the auspice of the U.S.A
The General Image Quality Equation (GIQE) is a mathematical tool that allows the NIIRS rating of an image to be computed as a function of parameters that describe sensor performance during image capture and, eventually, parameters that characterize processing techniques applied to the raw image
This result is coherent with the observations that GIQE 4 is inaccurate for low Signal Difference to Noise Ratio (SDNR) values (Thurman, 2008) and that that for low Signal to Noise Ratio” (SNR) the quality of the raw image is better than the one of the processed image (Auelmann, 2012)
Summary
The National Image Interpretability Rating Scale (NIIRS) was developed in the 1970 ́s under the auspice of the U.S.A. NIIRS is a ten-level integer scale (from 0 to 9), that allows image interpreters to rank an image according to its interpretability defined as the usefulness to perform certain specific Detection, Recognition and Identification (DRI) tasks. The complexity of these tasks increases with the rating scale, for example regarding automobiles, an image with rating scale 6 (NIIRS level 6), allows their classification as either sedan or station wagons, whereas a NIIRS level 8 image, allows the detection of their windshield wipers. A “general” equation that models these ratings as a function of the capture and – eventually – processing parameters is derived
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