Slanted edge MTF measurement as per ISO12233 is the defacto standard for measuring camera sharpness at manufacturing end of line. MTF measured by slanted edge has a number of advantages for measuring sharpness, being scale invariant, and relatively robust to geometric distortion. However, slanted edge MTF measurement is known to be affected by image processing algorithms, including demosaic, edge enhancement, and denoise algorithms. To avoid these confounding factors, it is increasingly common to measure MTF directly from the raw sensor image. This approach is logical if you are assessing the optomechanical lens-imager alignment and focus. However, end-of-line production testing has specific requirements, including speed of execution, repeatability and reproducibility. These requirements are typically not considered when configuring a camera for end-of-line MTF measurement. In this study, the execution time, repeatability and reproducibility of MTF measurement for multiple image capture and image processing combinations are examined.
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.
The Natural Scene derived Spatial Frequency Response (NS-SFR) framework automatically extracts suitable step-edges from natural pictorial scenes and processes these edges via the edge-based ISO12233 (e-SFR) algorithm. Previously, a novel methodology was presented to estimate the standard e-SFR from NS-SFR data. This paper implements this method using diverse natural scene image datasets from three characterized camera systems. Quantitative analysis was carried out on the system e-SFR estimates to validate accuracy of the method. Both linear and non-linear camera systems were evaluated. To investigate how scene content and dataset size affect system e-SFR estimates, analysis was conducted on entire datasets, as well as subsets of various sizes and scene group types. Results demonstrate that system e-SFR estimates strongly correlate with results from test chart inputs, with accuracy comparable to that of the ISO12233. Further work toward improving and fine-tuning the proposed methodology for practical implementation is discussed.
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.
Spatial image quality metrics designed for camera systems generally employ the Modulation Transfer Function (MTF), the Noise Power Spectrum (NPS) and a visual contrast detection model. Prior art indicates that scene-dependent characteristics of non-linear, content-aware image processing are unaccounted for by MTFs and NPSs measured by traditional methods. The authors present two novel metrics: the log Noise Equivalent Quanta (log NEQ) and Visual log NEQ. They both employ Scene-and-Process-Dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures, which account for signal transfer and noise scene dependency, respectively. The authors also investigate implementing contrast detection and discrimination models that account for scene-dependent visual masking. Also, three leading camera metrics are revised to use the above scene-dependent measures. All metrics are validated by examining correlations with the perceived quality of images produced by simulated camera pipelines. Metric accuracy improved consistently when the SPD-MTFs and SPD-NPSs were implemented. The novel metrics outperformed existing metrics of the same genre.