Scene-and-Process-Dependent Spatial Image Quality Metrics
This work addresses the need for more accurate image quality assessment in camera systems, particularly for non-linear processing, but it is incremental as it builds on existing metric frameworks.
The paper tackled the problem that traditional spatial image quality metrics for cameras do not account for scene-dependent effects in non-linear image processing, by introducing novel metrics (log NEQ and Visual log NEQ) using scene-and-process-dependent MTF and NPS measures, which improved accuracy in correlating with perceived image quality compared to existing metrics.
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.