Perceptual quality assessment has long been organized around a single number: the Mean Opinion Score. In this perspective piece, we argue that this target is too narrow, and that the next generation of quality assessment models should integrate context, reasoning, and multimodal understanding rather than regressing to one scalar.

The problem with collapsing quality into a single MOS is that it discards almost everything about why something looks good or bad. A number cannot say which distortion is present, whether it matters for the task at hand, or how the intended use changes what “good” means. As quality assessment moves into richer settings, this loss of context and explanation becomes a real limitation.

Our position is that quality models should reason about content and context, draw on multiple modalities including language, and produce assessments that are interpretable rather than opaque scores. Recent advances in multimodal models and language-based reasoning make this shift feasible, allowing quality to be described and justified, not merely predicted.

We hope this reframes quality assessment as a reasoning problem about perception and utility, pointing toward models that explain their judgments and adapt to context. This was joint work with M. Tliba, A. Chetouani, N. Aburaed, and A. Bruno.

See the paper for the full methodology and results.