# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1907.08926