The edge-based Spatial Frequency Response (e-SFR) is an established measure for camera system quality performance, traditionally measured under laboratory conditions. With the increasing use of Deep Neural Networks (DNNs) in autonomous vision systems, the input signal quality becomes crucial for optimal operation. This paper proposes a method to estimate the system e-SFR from pictorial natural scene derived SFRs (NSSFRs) as previously presented, laying the foundation for adapting the traditional method to a real-time measure.In this study, the NS-SFR input parameter variations are first investigated to establish suitable ranges that give a stable estimate. Using the NS-SFR framework with the established parameter ranges, the system e-SFR, as per ISO 12233, is estimated. Initial validation of results is obtained from implementing the measuring framework with images from a linear and a non-linear camera system. For the linear system, results closely approximate the ISO 12233 e-SFR measurement. Non-linear system measurements exhibit scene-dependant characteristics expected from edge-based methods. The requirements to implement this method in real-time for autonomous systems are then discussed.
Oliver van Zwanenberg, Sophie Triantaphillidou, Robin B. Jenkin, Alexandra Psarrou, "Natural Scene Derived Camera Edge Spatial Frequency Response for Autonomous Vision Systems" in Proc. IS&T London Imaging Meeting 2021: Imaging for Deep Learning, 2021, pp 88 - 92, https://doi.org/10.2352/issn.2694-118X.2021.LIM-88