Title | Noise Power Spectrum Scene-Dependency in Simulated Image Capture Systems |
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Authors | Fry, E., Triantaphillidou, S., Jenkin, R., Jacobson, R.E. and Jarvis, J. |
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Editors | Bonnier, Nicolas and Farias, Mylène |
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Type | Conference paper |
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Abstract | The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply non- linear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The scene- and-process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-process- dependent Modulation Transfer Functions (SPD-MTF). |
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Keywords | Noise Power Spectrum |
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| NPS |
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| scene and process dependent |
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| SPD-NPS |
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| camera performance |
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Year | 2020 |
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Conference | IS&T Electronic Imaging: Image Quality and System Performance XVII |
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Publisher | The Society of Imaging Science and Technology |
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Accepted author manuscript | |
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Publisher's version | License CC BY 4.0 File Access Level Open (open metadata and files) |
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Publication dates |
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Published | 26 Jan 2020 |
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Journal | Electronic Imaging |
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Journal citation | XVII, pp. 345-1-345-7 |
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ISSN | 2470-1173 |
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Digital Object Identifier (DOI) | https://doi.org/10.2352/ISSN.2470-1173.2020.9.IQSP-345 |
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Web address (URL) of conference proceedings | https://www.ingentaconnect.com/content/ist/ei |
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