{"schema":"record/v1","id":"art_01M5MTXH6HN52FB185VESXGDQB","type":"article","revision":1,"etag":"\"r1.s0\"","title":"How should upscaled footage be judged: what do primary sources say about full-reference versus no-reference metrics and their limits?","summary":"Blau and Michaeli prove that lower distortion (full-reference measures such as PSNR/SSIM) and better perceptual quality are at odds for any distortion measure, which is why GAN-based upscalers can score worse on PSNR while looking better. Real-ESRGAN reports only the no-reference NIQE and warns such metrics do not track human preference. The VMAF docs describe a full-reference metric trained on subjective data for stated viewing conditions, say it captures gains from enhancement operations, and provide a NEG mode that disables enhancement gain for codec comparisons; its FAQ shows upscaling the distorted clip before comparison. No inspected source states a validated protocol for judging AI-upscaled finishing footage; broadcaster specs require QC but no metric thresholds in the passages read.","status":"active","body":{"text":"Blau and Michaeli prove that lower distortion (full-reference measures such as PSNR/SSIM) and better perceptual quality are at odds for any distortion measure, which is why GAN-based upscalers can score worse on PSNR while looking better. Real-ESRGAN reports only the no-reference NIQE and warns such metrics do not track human preference. The VMAF docs describe a full-reference metric trained on subjective data for stated viewing conditions, say it captures gains from enhancement operations, and provide a NEG mode that disables enhancement gain for codec comparisons; its FAQ shows upscaling the distorted clip before comparison. No inspected source states a validated protocol for judging AI-upscaled finishing footage; broadcaster specs require QC but no metric thresholds in the passages read.\n\nCurator qualifications: Excerpted evidence located (one excerpt-less row); perception-distortion trade-off (Blau & Michaeli); VMAF tracking subjective quality vs rewarding enhancement kept (use-scope difference).\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/1711.06077v4","note":"candidate evidence excerpts"},{"url":"https://github.com/Netflix/vmaf/blob/272c5967701017b90a0ebcff24205e59dbba6421/resource/doc/models_v0.md","note":"candidate evidence excerpts"},{"url":"https://github.com/Netflix/vmaf/blob/272c5967701017b90a0ebcff24205e59dbba6421/resource/doc/faq.md","note":"candidate evidence excerpts"},{"url":"https://github.com/Netflix/vmaf/blob/272c5967701017b90a0ebcff24205e59dbba6421/resource/doc/models_v1.md","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2107.10833v2","note":"candidate evidence excerpts"},{"url":"https://github.com/hzwer/Practical-RIFE/blob/bbfd2ea90910789a860ea3e2b32a240cd577b75e/README.md","note":"candidate evidence excerpts"}]},"categories":["T10","T12","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/abs/1711.06077v4","claim":"Blau and Michaeli prove that lower distortion (full-reference measures such as PSNR/SSIM) and better perceptual quality are at odds for any distortion measure, which is why GAN-based upscalers can score worse on PSNR while looking better. 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