{"id":"W3127385052","doi":"10.48550/arxiv.2102.00195","title":"Quantifying Visual Image Quality: A Bayesian View","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vision science; Computer science; Artificial intelligence; Human visual system model; Data science; Image processing; Image quality; Quality (philosophy); Computer graphics; Perception; Graphics; Bridging (networking); Bayesian probability; Perspective (graphical); Human–computer interaction; Computer vision; Image (mathematics); Computer graphics (images); Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01107619,0.001597348,0.001863784,0.006518848,0.001075701,0.007248136,0.003632375,0.003586273,0.002699652],"category_scores_gemma":[0.0367694,0.001461206,0.001428266,0.002526419,0.009409624,0.01043787,0.004475141,0.004461299,0.0007747484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003344286,"about_ca_system_score_gemma":0.001873016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007806628,"about_ca_topic_score_gemma":0.003997946,"domain_scores_codex":[0.9946482,0.002144852,0.0002848425,0.0009605118,0.001780337,0.0001813343],"domain_scores_gemma":[0.9820678,0.01140018,0.001559773,0.001725986,0.002769528,0.0004767431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005526755,0.00004729719,0.002318742,0.0004511318,0.0002296673,0.00009017839,0.0004091125,0.06449829,0.002416958,0.8213571,0.003543003,0.1045832],"study_design_scores_gemma":[0.00001730044,0.00004309151,0.001332793,0.0002063174,0.00006710439,0.0001828249,0.00009056393,0.1255285,0.001076368,0.8636713,0.007701499,0.00008226006],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002748154,0.003824268,0.9851155,0.00248074,0.00006763725,0.00003817931,0.0001299991,0.0001286499,0.005466919],"genre_scores_gemma":[0.4014006,0.0168286,0.5714725,0.001950315,0.001545861,0.0004175538,0.0004759319,0.0003144083,0.005594214],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01107619,"threshold_uncertainty_score":0.05857712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1814833187489943,"score_gpt":0.2986726276703497,"score_spread":0.1171893089213554,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}