{"id":"W2972931163","doi":"10.1109/access.2019.2941112","title":"Learning a No-Reference Quality Predictor of Stereoscopic Images by Visual Binocular Properties","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer vision; Stereoscopy; Computer science; Monocular; Feature (linguistics); Binocular rivalry; Histogram; Pattern recognition (psychology); Human visual system model; Normalization (sociology); Feature extraction; Contrast (vision); Visual perception; Image (mathematics); Perception","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0005163634,0.0001768936,0.0003256899,0.00007548656,0.00007614595,0.0003825266,0.001408341,0.00007066991,0.00004819962],"category_scores_gemma":[0.00006405628,0.0001445664,0.00006470013,0.0002349999,0.00006742816,0.001885881,0.0004093835,0.0002271066,0.0001626436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003491319,"about_ca_system_score_gemma":0.0001228217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006921986,"about_ca_topic_score_gemma":0.000005212513,"domain_scores_codex":[0.9979225,0.0003346171,0.0004472044,0.0004508794,0.0005351351,0.0003096889],"domain_scores_gemma":[0.9987803,0.00009171631,0.0002604742,0.0005398712,0.0002554397,0.00007224071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001088496,0.0008207161,0.2366699,0.001893181,0.0001921617,0.00001099003,0.002536418,0.0002219332,0.7344326,0.00238514,0.003621747,0.01710633],"study_design_scores_gemma":[0.001720517,0.001106911,0.0441561,0.0004264208,0.00002540972,0.000003343818,0.0003169273,0.01469415,0.9327543,0.0003436553,0.003634182,0.0008181301],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9244967,0.0001791841,0.07312766,0.000139938,0.0004270238,0.0002953619,0.0000072961,0.0001241298,0.00120278],"genre_scores_gemma":[0.9971974,0.00001982697,0.0009739299,0.0002121484,0.00004542019,0.0000240154,0.000004518886,0.00001072406,0.001511966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1983216,"threshold_uncertainty_score":0.5895244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05046259265367067,"score_gpt":0.3589707742729955,"score_spread":0.3085081816193249,"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."}}