Scalable Evaluation of the Realism of Synthetic Environmental Augmentations in Images
Evaluation of AI systems often requires synthetic test cases, particularly for rare or safety-critical conditions that are difficult to observe in operational data. Generative AI offers a promising approach for producing such data through controllable image editing, but its usefulness depends on whether the resulting images are sufficiently realistic to support meaningful evaluation. We present a scalable framework for assessing the realism of synthetic image-editing methods and apply it to the task of adding environmental conditions-fog, rain, snow, and nighttime-to car-mounted camera images. Using 40 clear-day images, we compare rule-based augmentation libraries with generative AI image...
| Authors | Damian J. Ruck, Paul Vautravers, Oliver Chalkley et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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