| Abstract | Camera Image Quality Metrics (IQMs) are widely used to characterise imaging systems for human as well as machine vision applications, yet their relationship to the latter remains insufficiently understood. Recently introduced image information metrics have been proposed as alternatives to traditional IQMs, but their behavior under controlled imaging degradations and relevance to vision tasks require further study. This work analyzes the behavior of traditional camera IQMs and image information metrics under controlled blur and noise. We collected a dataset consisting of two parts. The first consists of laboratory captures of four objects, created with systematic camera variations in defocus, noise level, exposure value, and camera-to-object distance. The images included a test chart to enable direct measurement of imaging metrics from each scene. For the second part, a physicsbased imaging pipeline simulation was used to generate synthetic images with independently controlled blur and noise levels. Across both laboratory and simulated data, image information metrics exhibited greater sensitivity to the combined effect of optical and noise degradations compared to traditional IQMs. Multiple object detection networks were evaluated and qualitative relationships between traditional IQMs, image information metrics, and detection performance were examined. Although detection performance variations were modest, certain image information metrics, particularly the ideal observer signal-to-noise ratio, exhibited qualitatively more consistent monotonic trends than traditional IQMs. Overall, the results indicate that image information metrics show greater sensitivity to combined blur and noise degradations than traditional IQMs, while also highlighting the limitations of imaging metrics in predicting task-level performance, which remains content dependent. |
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