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 nonlinear 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 sceneand- 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-processdependent Modulation Transfer Functions (SPD-MTF).
Highlights
Spatial luminance contrast signals are core to subjective impressions of image quality and its attributes of resolution, noise, sharpness and contrast
The Noise Power Spectrum (NPS) characterizes luminance noise with respect to spatial frequency. It is used routinely in the design and optimization of capture systems. It is a fundamental component of spatial image quality metrics (IQM) that aim to correlate with the perceived image quality [1]
The utility of several SPD-NPS measures and metrics has been demonstrated by characterizing the noise power of four simulated camera pipelines that apply linear and non-linear image signal processes (ISP) under various exposure conditions
Summary
Spatial luminance contrast signals are core to subjective impressions of image quality and its attributes of resolution, noise, sharpness and contrast. Non-linear content-aware sharpening filters enhance local contrast selectively to minimize the perceived amplification of noise [7,8,9,10,11] They amplify noise to a greater extent in regions containing edges, detail and other structural signals than in uniform luminance areas. In non-linear systems, deriving the NPS from uniform patches, or any single test chart, cannot characterize [4]: 1) the system noise power with respect to a given input scene; 2) the average real-world system noise power, while accounting for the system’s scene-dependency; 3) the level of scene-dependency in the noise power of the system. The dead leaves SPD-NPS approximates the average real-world system noise power It accounts for system scene-dependency to a limited extent only, but is more appropriate than the uniform patch NPS [4]. This requires that the image set is representative of commonly captured scenes, and the individual pictorial image SPD-NPSs are accurately measured
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