{"id":"W4315473471","doi":"10.1109/tip.2023.3234498","title":"Degraded Reference Image Quality Assessment","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Distortion (music); Computer science; Pipeline (software); Image quality; Source code; Quality (philosophy); Data mining; Artificial intelligence; Information retrieval; Image (mathematics)","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":[0.001084248,0.0003082743,0.000323092,0.0003563685,0.0007739547,0.000997634,0.00089987,0.0001032325,0.00005991031],"category_scores_gemma":[0.00001741582,0.0002991887,0.0001475116,0.001538901,0.0001293475,0.002435535,0.00001414985,0.0005905929,0.0004753189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001778714,"about_ca_system_score_gemma":0.0004026818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000651946,"about_ca_topic_score_gemma":0.0000193369,"domain_scores_codex":[0.9969189,0.000292286,0.0005873725,0.0007906976,0.000780082,0.0006306818],"domain_scores_gemma":[0.9983243,0.0002158467,0.0001930859,0.0008131698,0.0002781146,0.0001754718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003707187,0.0008771298,0.00002352181,0.0005725886,0.00007492784,0.0001465037,0.002498872,0.0005528342,0.1877756,0.002651507,0.001773532,0.8030159],"study_design_scores_gemma":[0.003486454,0.0005572165,0.008377367,0.0006622571,0.0001283282,0.00008509913,0.002683322,0.3862484,0.5765483,0.01442623,0.00392435,0.002872676],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003210779,0.00002375816,0.9874274,0.003147938,0.0003735789,0.000236136,0.00001778372,0.001316627,0.004246041],"genre_scores_gemma":[0.8153316,0.00003895132,0.18247,0.0005977578,0.00004395755,0.0001293425,0.000007335304,0.00003045539,0.001350565],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8121209,"threshold_uncertainty_score":0.999946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1042840251152523,"score_gpt":0.3976023353464866,"score_spread":0.2933183102312343,"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."}}