Scene-and-Process-Dependent Spatial Image Quality Metrics

TL;DR

This paper introduces scene-and-process-dependent spatial image quality metrics, SPD-NEQ and Visual log NEQ, which improve correlation with perceived image quality by accounting for scene-specific processing effects.

Contribution

The paper presents novel scene-dependent metrics (SPD-NEQ and Visual log NEQ) that enhance image quality assessment accuracy over traditional methods.

Findings

Scene-dependent measures improve metric accuracy

Novel metrics outperform existing ones

Enhanced correlation with perceived quality

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 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.