Scene-and-Process-Dependent Spatial Image Quality Metrics

Abstract

Abstract 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.