Recent research on digital camera performance evaluation introduced the Natural Scene Spatial Frequency Response (NS-SFR) framework, shown to provide a comparable measure to the ISO12233 edge SFR (e-SFR) but derived outside laboratory conditions. The framework extracts step-edges captured from pictorial natural scenes to evaluate the camera SFR. It is in 2-parts. The first utilizes the ISO12233 slanted-edge algorithm to produce an ‘envelope’ of NS-SFRs. The second estimates the system e-SFR from this NS-SFR data. One drawback of this proposed methodology has been the computation time. The process was not optimized, as it first derived NS-SFRs from all suitable step-edges and then further validated and statistically treated the results to estimate the e-SFR. This paper presents changes to the framework processes, aiming to optimize the computation time so that it is practical for real-world implementation. The developments include an improved framework structure, a pixel-stretching filter alternative, and the capability to utilize Graphics Processing Unit (GPU) acceleration. In addition, the methodology was updated to utilize the latest e-SFR algorithm implementation. The resulting code has been incorporated into a self-executable user interface prototype, available in GitHub. Future goals include making it an open-access, cloud-based solution to be used by scientists, camera evaluation labs and the general public.
The Modulation Transfer Function (MTF) is a wellestablished measure of camera system performance, commonly employed to characterize optical and image capture systems. It is a measure based on Linear System Theory; thus, its use relies on the assumption that the system is linear and stationary. This is not the case with modern-day camera systems that incorporate non-linear image signal processes (ISP) to improve the output image. Nonlinearities result in variations in camera system performance, which are dependent upon the specific input signals. This paper discusses the development of a novel framework, designed to acquire MTFs directly from images of natural complex scenes, thus making the use of traditional test charts with set patterns redundant. The framework is based on extraction, characterization and classification of edges found within images of natural scenes. Scene derived performance measures aim to characterize non-linear image processes incorporated in modern cameras more faithfully. Further, they can produce ‘live’ performance measures, acquired directly from camera feeds.