{"id":"W2896214331","doi":"10.1007/s11042-018-6759-x","title":"Perceptual quality assessment of stereoscopic images based on local and global visual characteristics","year":2018,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Stereoscopy; Computer science; Artificial intelligence; Computer vision; Pooling; Image quality; Quality (philosophy); Perception; Quality Score; Metric (unit); Consistency (knowledge bases); Subjective video quality; Quality assessment; Image (mathematics)","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":[0.0002787532,0.0001294472,0.0002069198,0.00003136173,0.0001489937,0.0001451161,0.0002177326,0.00005331648,0.00002004813],"category_scores_gemma":[0.00002410208,0.0001165002,0.00002952136,0.0001434592,0.0003483837,0.0001967621,0.0001413928,0.00008110229,0.000009840344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000423162,"about_ca_system_score_gemma":0.000095658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005213526,"about_ca_topic_score_gemma":0.000008375133,"domain_scores_codex":[0.9988356,0.00009135366,0.0003122509,0.0003517763,0.0002374121,0.000171558],"domain_scores_gemma":[0.9990487,0.0002292246,0.0001248086,0.0003589623,0.0001259136,0.0001124373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001277293,0.0006025381,0.02312287,0.00009597066,0.00002033246,0.000001155034,0.0003219443,0.000003178765,0.002344312,0.02740436,0.0001787437,0.9458918],"study_design_scores_gemma":[0.0006039109,0.0003165152,0.8387599,0.000027009,0.00001405152,0.000001518679,0.0001753333,0.1569176,0.001016252,0.0003361632,0.001640739,0.000191032],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05488838,0.00001363669,0.9431098,0.0006190796,0.00004959845,0.0003244217,0.0001601733,0.00004335317,0.0007915847],"genre_scores_gemma":[0.9273351,0.00001079257,0.07194152,0.0004723858,0.00009325799,0.0000893661,0.00003369176,0.000004129242,0.00001969195],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9457008,"threshold_uncertainty_score":0.475074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0445807367605793,"score_gpt":0.3869526852577924,"score_spread":0.3423719484972131,"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."}}