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13 May 2019 Quantitative assessment of image quality for maritime surveillance applications
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Analysis and measurement of perceived image quality has been an active area of research for decades. Although physical measurements of image parameters often correlate with human perceptions, user-centric approaches have focused on the observer’s ability to perform certain tasks with the imagery. This task-based orientation has led to the development of the Johnson Criteria and the National Imagery Interpretability Ratings Scale as standards for quantifying the interpretability of an image. A substantial literature points to three primary factors affecting human perception of image interpretability: spatial resolution, image sharpness as measured by the relative edge response, and perceived noise measured by the signal-to-noise ratio. For maritime and ocean surveillance applications, however, these factors do not fully represent the characteristics of the imagery. Images looking at the ocean surface can encompass a wide range of spatial resolutions. Fog, sun glint, and color distortion can degrade image interpretability. In this paper, we explore both the general factors and the domain specific concerns for quantifying image interpretability. In particular, we propose new metrics to assess the dynamic range and color balance for maritime surveillance imagery. We will present the new metrics and illustrate their performance on relevant image data.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ross Eaton, Ian M. Gingrich, and John M. Irvine "Quantitative assessment of image quality for maritime surveillance applications", Proc. SPIE 10992, Geospatial Informatics IX, 109920L (13 May 2019);

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