Camera Scene Grammars: Reference-Free Semantic Quality Assessment for Fixed-View Surveillance

Surveillance streams are monitored through scalar quality scores, yet a single score cannot say which observations in the scene remain dependable, whether a degradation has removed a person, introduced a phantom one, or turned a parked vehicle into a moving one. We argue that a fixed camera carries a resource that isolated-image quality assessment lacks, namely its own earlier observations of the same view, and we formulate history-conditioned semantic reliability as the task of judging a degraded stream against that history without any contemporaneous clean reference. A camera scene grammar G_c stores persistent anchors, class-conditioned occupancy, track continuity, and relation context estimated from undistorted history. Comparing the stream with its grammar yields three typed evidence channels, missing expected content, unsupported observations, and inadmissible relations, together with an explicit coverage mask that marks where the history cannot support a judgment, and a grammar-conditioned student amortizes the comparison using supervision derived from clean and degraded pairs without opinion scores. We give an exact error decomposition for ensemble consensus under shared generator bias, conditional monotonicity and stability guarantees for the channels, and an identifiability limit that bounds what any history can recover. Under a camera-disjoint protocol on the 50-camera D-CQA corpus with independently annotated object-level errors, the scalar readout reaches 0.903 SRCC, per-channel attribution reaches 0.881, 0.902, and 0.858 AUROC for dropped, hallucinated, and misrelated content, and matched-history baselines, a different camera's grammar, and a spatially permuted grammar each recover only part of the gain, isolating camera-specific semantic structure as its source. We release the graph annotation layer D-CQA-SG.