Validation of Modulation Transfer Functions and Noise Power Spectra from Natural Scenes

TL;DR

This paper introduces and validates new scene-dependent measures for MTF and NPS that better characterize the performance of imaging systems on natural scenes, addressing limitations of traditional methods.

Contribution

The paper develops novel scene-and-process-dependent MTF and NPS measures, validated as more robust and representative for scene-dependent imaging systems than existing traditional measures.

Findings

Proposed measures are robust and preferable for scene-dependent systems.

Validated new measures using natural scenes and dead leaves chart.

Improved characterization of system performance on real-world images.

Abstract

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.