Many of the metrics developed for informational imaging are useful in automotive imaging, since many of the tasks – for example, object detection and identification – are similar. This work discusses sensor characterization parameters for the Ideal Observer SNR model, and elaborates on the noise power spectrum. It presents cross-correlation analysis results for matched-filter detection of a tribar pattern in sets of resolution target images that were captured with three image sensors over a range of illumination levels. Lastly, the work compares the crosscorrelation data to predictions made by the Ideal Observer Model and demonstrates good agreement between the two methods on relative evaluation of detection capabilities.
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. However, they are applied routinely to scene-dependent systems applying non-linear, content-aware image signal processing. For such systems, MTFs/NPSs are derived inaccurately from traditional test charts containing edges, sinusoids, noise or uniform luminance signals, which are unrepresentative of natural scene signals. The dead leaves test chart delivers improved measurements from scene-dependent systems but still has its limitations. In this article, the authors validate novel scene-and-process-dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures that characterize (i) system performance concerning one scene, (ii) average real-world performance concerning many scenes or (iii) the level of system scene dependency. The authors also derive novel SPD-NPS and SPD-MTF measures using the dead leaves chart. They demonstrate that the proposed measures are robust and preferable for scene-dependent systems to current measures.