Q-Harness: Few-Shot No-Reference Image Quality Assessment as a Scalable Skill

No-reference image quality assessment (NR-IQA) is often needed in settings where a new camera pipeline, restoration method, compression codec, or generative model creates a new target domain faster than a large mean-opinion-score (MOS) database can be collected. In such deployments, NR-IQA should be few-shot by design: a practical assessor should adapt from only a few labeled examples and be ready to use. Existing per-dataset fine-tuning does not match this need, because it spends scarce labels re-estimating a large perceptual model, overfits target-domain statistics, and must be repeated for every new domain. We instead frame NR-IQA as a scalable skill of foundation models: modern vision-language models (VLMs) already contain strong quality priors, and the right interface is to elicit and calibrate this skill rather than retrain it. We present Q-Harness, a harness system that turns a frozen general-purpose VLM into a few-shot quality assessor without backbone training. Given a small set of expert labels, it elicits interpretable per-attribute quality subscores (sharpness, noise, exposure, composition, etc.) and maps them to a final MOS through a lightweight ridge calibration layer fitted on those labels. A controlled ablation shows that the harness's gain is semantic: querying the same construct in multiple ways yields little benefit, irrelevant attributes receive near-zero weight, and performance improves as quality attributes become less redundant.