{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009859771,0.0005016017,0.0007418737,0.0002775078,0.0002835519,0.0009454942,0.002256333,0.0003423109,0.0001136744],"category_scores_gemma":[0.00008763982,0.000610035,0.000528357,0.0008917549,0.0001270287,0.001195483,0.00446848,0.0009387059,0.0001278818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003109956,"about_ca_system_score_gemma":0.0007010892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006854385,"about_ca_topic_score_gemma":0.0002303527,"domain_scores_codex":[0.9958028,0.0009274795,0.0005120146,0.001876317,0.000256284,0.0006251631],"domain_scores_gemma":[0.996712,0.0002251975,0.0004490118,0.001989617,0.0003484222,0.0002757479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008487555,0.001950711,0.005986316,0.003612723,0.00117542,0.01098864,0.005184283,0.01207329,0.002882063,0.9306602,0.001324032,0.02407737],"study_design_scores_gemma":[0.001954965,0.0002017137,0.006270189,0.001432635,0.0003514664,0.0000473813,0.003183863,0.9481085,0.003471321,0.02806641,0.003246754,0.003664827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08480795,0.000266982,0.9108746,0.0004663541,0.0007425554,0.0002908286,0.000008929769,0.0003720942,0.002169695],"genre_scores_gemma":[0.9817542,0.000348005,0.01629276,0.0006514334,0.0001132179,0.000002066322,0.00004435911,0.00002938836,0.0007645335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9360352,"threshold_uncertainty_score":0.9996351,"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."}}