Image degradations arise throughout acquisition, processing, and transmission, altering both visual appearance and downstream machine-perception performance. They are studied across synthetic corruption benchmarks, perceptual image quality assessment (IQA), low-level vision, and physically grounded imaging-system analysis. However, these communities rely on incompatible grouping schemes and backend-specific severity definitions, making results difficult to compare across datasets, degradation models, and tasks. We propose a causally grounded framework for organizing and interpreting image degradations. Rather than introducing new degradations or redefining existing benchmarks, we provide an interpretive representation and measurement layer that makes implicit assumptions explicit. Each degradation is characterized by two orthogonal axes: its dominant causal source in the imaging pipeline and its dominant perceptual effect. This dual-axis abstraction yields a compact taxonomy spanning algorithmic corruptions, perceptual distortions, low-level imaging artifacts, and physically motivated camera or system failures. To make severity semantics observable without modifying existing implementations, we quantify each native severity level using full-reference image quality metrics: PSNR, SSIM, and LPIPS. This separates causal origin, perceptual appearance, measured distortion strength, and downstream task impact while preserving native parameterizations. We demonstrate the framework through COCO-Degradation, a taxonomy-aligned benchmark for evaluating object detector robustness under diverse low-level imaging conditions. By connecting causal source, perceptual effect, and measured severity, the proposed framework supports more interpretable and reproducible evaluation of how low-level visual degradations affect machine perception.

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