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)
