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 but are applied routinely to systems employing non-linear, content-aware image processing. For such systems, MTFs/NPSs are derived inaccurately from traditional test charts containing edges, sinusoids, noise or uniform tone signals, which are unrepresentative of natural scene signals. The dead leaves test chart delivers improved measurements, but still has limitations when describing the performance of scene-dependent systems. In this paper, we validate several novel scene-and-process-dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures that characterize, either: i) system performance concerning one scene, or ii) average real-world performance concerning many scenes, or iii) the level of system scene-dependency. We also derive novel SPD-NPS and SPD-MTF measures using the dead leaves chart. We demonstrate that all the proposed measures are robust and preferable for scene-dependent systems than current measures.
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.
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 using traditional methods. We 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. We also investigate implementing contrast detection and discrimination models that account for scene-dependent visual masking. Also, three leading camera metrics are revised that 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.MTF (SPD-MTF), Scene-and-Process-Dependent NPS (SPD-NPS), optimize their correlation with observer quality ratings from test image datasets that contain different types of artefacts. This paper is concerned specifically with no-reference spatial metrics suited for image capture systems engineering. Suitable IQMs break image quality judgement down into components relating to the different attributes, and the characteristics of imaging system components and the human visual system (HVS). A recent review [4] by the authors defines the following spatial IQM genres: Computational IQMs, Image Fidelity Metrics, Signal Transfer Visual IQMs (STV-IQM), and Multivariate Formalism (MF-IQM). When each genre was evaluated from a capture system engineering perspective, the Computational IQMs and Image Fidelity Metrics were concluded to be least suitable for the purpose [4]. The STV-IQMs and MF-IQMs -referred to in this paper as engineering metrics -employ standard spatial system performance measures such as the Modulation Transfer Function (MTF) and Noise Power Spectrum (NPS), and threshold contrast sensitivity functions (CSF) describing visual spatial sensitivity. The Noise Equivalent Quanta (NEQ) signal-to-noise measure is core to the most relevant STV-IQMs and is applied widely in capture system and sensor modelling [6]-[8]; it also uses the MTF and NPS.Our recent evaluation of simulated camera pipelines, however, revealed that the currently employed MTF and NPS measures characterize systems using non-linear content-aware image signal processing (ISP) with limited accuracy, and that novel Scene-and-Process-Dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures are more suitable [9]. Likewise, contextual contrast detection [10] and discrimination [11] models, which account for each scene's contrast spectrum, should be more suitable visual models for image quality analysis than the currently used CSFs.This paper aims to revise curre...
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.
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