Estimation of ISO12233 Edge Spatial Frequency Response from Natural Scene Derived Step-Edge Data

Abstract

The Natural Scene derived Spatial Frequency Response (NS-SFR) is a novel camera system performance measure that derives SFRs directly from images of natural scenes and processes them using ISO12233 edge-based SFR (e-SFR) algorithm. NS-SFR is a function of both camera system performance and scene content. It is measured directly from captured scenes, thus eliminating the use of test charts and strict laboratory conditions. The effective system e-SFR can be subsequently estimated from NS-SFRs using statistical analysis and a diverse dataset of scenes. This paper first presents the NS-SFR measuring framework, which locates, isolates, and verifies suitable step-edges from captures of natural scenes. It then details a process for identifying the most likely NS-SFRs for deriving the camera system e-SFR. The resulting estimates are comparable to standard e-SFRs derived from test chart inputs, making the proposed method a viable alternative to the ISO technique, with potential for real-time camera system performance measurements.

Analysis of Natural Scene Derived Spatial Frequency Responses for Estimating Camera ISO12233 Slanted-edge Performance

“…This paper provides a quantitative benchmark of the methodology presented in Ref. [1] and elucidates findings from previous studies published [3][4][5], all by the same authors. The system e-SFR estimation accuracy and precision are reported, and its compatibility with the standard is discussed.…”

“…This section provides a short overview of Ref. [1], i.e., the methodology used to extract NS-SFRs from natural scene captures and the subsequent use of this data to estimate the ISO12233 camera system e-SFR [1].…”

“…Then, the resultant binary edge map is used to crop regions of interest (ROIs) with appropriate window sizes. A pixel stretching filter was developed to improve the yield of edges isolated from the images [1,4]. This filter removes unwanted artifacts from either side of the edge of interest, such as noise, scene textures and illumination non-uniformities, shown to confound the resulting e-SFRs [1].…”

“…A pixel stretching filter was developed to improve the yield of edges isolated from the images [1,4]. This filter removes unwanted artifacts from either side of the edge of interest, such as noise, scene textures and illumination non-uniformities, shown to confound the resulting e-SFRs [1]. Each ROI then goes through a step-edge verification process [1], analyzing the edge gradient to determine if the edge of interest is of the correct profile.…”

“…This paper presents an evaluation of a methodology presented in Ref. [1] that automatically extracts camera Natural Scene derived Spatial Frequency Responses (NS-SFRs) from a camera dataset. The NS-SFRs are computed from extracted natural scene step-edges via the ISO12233 e-SFR algorithm [2].…”