Imaging system performance measures and Image Quality Metrics (IQM) are reviewed from a systems engineering perspective, focusing on spatial quality of still image capture systems. We classify IQMs broadly as: Computational IQMs (CP-IQM), Multivariate Formalism IQMs (MF-IQM), Image
Fidelity Metrics (IF-IQM), and Signal Transfer Visual IQMs (STV-IQM). Comparison of each genre finds STV-IQMs well suited for capture system quality evaluation: they incorporate performance measures relevant to optical systems design, such as Modulation Transfer Function (MTF) and Noise-Power
Spectrum (NPS); their bottom-up, modular approach enables system components to be optimized separately. We suggest that correlation between STV-IQMs and observer quality scores is limited by three factors: current MTF and NPS measures do not characterize scene-dependent performance introduced
by imaging system non-linearities; contrast sensitivity models employed do not account for contextual masking effects; cognitive factors are not considered. We hypothesize that implementation of scene and process-dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures should mitigate errors originating
from scene dependent system performance. Further, we propose implementation of contextual contrast detection and discrimination models to better represent low-level visual performance in image quality analysis. Finally, we discuss image quality optimization functions that may potentially close
the gap between contrast detection/discrimination and quality.
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IMAGE QUALITY METRIC;
Document Type: Research Article
January 1, 2018
This article was made available online on January 13, 2018 as a Fast Track article with title: "Bridging the gap between imaging performance and image quality measures".
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