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.

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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...

What is the best luminance contrast weighting-function for image quality optimization? 'Traditional' contrast sensitivity functions (CSFs) have been applied as weighting-functions in image difference metrics. This weighting also resulted in increased sharpness and color-preference, according to previous psychophysical research. We suggest 'contextual' CSFs (cCSFs) and 'contextual' discrimination functions (cVPFs) should provide bases for further improvement; these functions are directly measured from pictorial scenes, modeling threshold and suprathreshold sensitivities within the context of complex masking information. Image quality assessment is understood to require detection/discrimination of masked signals, making 'contextual' CSFs directly relevant.In this investigation, images are weighted with a 'traditional' CSF, cCSF, cVPF and a 'constant' function. Controlled mutations of these functions are also applied as weighting-functions, seeking the optimal band weighting for quality optimization. Image quality, sharpness and naturalness are then assessed in two-alternative forced-choice psychophysical tests. Maximal quality, results from cCSFs and cVPFs, mutated to boost contrast in the higher visible frequencies.

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Image Quality and System Performance proceedings.