What Does High-Resolution Supervision Actually Teach? Separating Source Support from Resolution Specialization

High-resolution super-resolution corpora are motivated by the assumption that natively high-resolution images supply supervision that lower-resolution data cannot. Existing comparisons cannot test it: replacing a low-resolution corpus with a native-4K one changes the degradation operator, the source pixels available for sampling, the number of scenes, the field of view of a fixed crop, the allocation of compute and the train–test match all at once, so the reported difference is a sum of six effects and identifies none of them. We measure the raster’s own contribution using same-source resolution pyramids, one declared degradation, a fixed optimisation budget, and evaluation of every arm both on a common test tier and across tiers. Source support, scene count and per-scene coverage are bound by one constraint, so no design varies the raster alone; we run all three that release one of them. On a source-rich corpus the apparent 4K advantage — 0.835 dB at 43M parameters — survives none of them, at any capacity or scale factor. On two unrelated RAW-derived corpora the preference between training rasters instead reverses with the target resolution. High-resolution data therefore supplies either additional source support or closer alignment with the deployment resolution, and the nominal raster measures neither.