  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.
