Validation of Modulation Transfer Functions and Noise Power Spectra from Natural Scenes
This work addresses the need for more accurate performance metrics in imaging systems that use non-linear processing, which is incremental as it builds on existing dead leaves chart methods.
The paper tackled the problem of inaccurately characterizing imaging system sharpness and noise for non-linear, content-aware systems using traditional test charts, by validating novel scene-and-process-dependent MTF and NPS measures that are robust and preferable for such systems.
The Modulation Transfer Function (MTF) and the Noise Power Spectrum (NPS) characterize imaging system sharpness/resolution and noise, respectively. Both measures are based on linear system theory but are applied routinely to systems employing non-linear, content-aware image processing. For such systems, MTFs/NPSs are derived inaccurately from traditional test charts containing edges, sinusoids, noise or uniform tone signals, which are unrepresentative of natural scene signals. The dead leaves test chart delivers improved measurements, but still has limitations when describing the performance of scene-dependent systems. In this paper, we validate several novel scene-and-process-dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures that characterize, either: i) system performance concerning one scene, or ii) average real-world performance concerning many scenes, or iii) the level of system scene-dependency. We also derive novel SPD-NPS and SPD-MTF measures using the dead leaves chart. We demonstrate that all the proposed measures are robust and preferable for scene-dependent systems than current measures.