{"id":"W1988868230","doi":"10.1109/icassp.2013.6638355","title":"3D video quality metric for mobile applications","year":2013,"lang":"en","type":"article","venue":"","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Metric (unit); Computer science; Distortion (music); Computer vision; Quality (philosophy); Video quality; Artificial intelligence; Subjective video quality; Mobile device; Image quality; Image (mathematics); Engineering; Computer network","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":[0.0004508651,0.00008138365,0.0001222503,0.0000863194,0.0001238794,0.0002341952,0.0006061809,0.0000341914,0.0001550239],"category_scores_gemma":[0.00003472071,0.00006770728,0.00007105697,0.0004755873,0.0000186594,0.0006436458,0.0001428996,0.00004593006,0.0005429126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003449477,"about_ca_system_score_gemma":0.00004736183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000315038,"about_ca_topic_score_gemma":0.000007774935,"domain_scores_codex":[0.9989936,0.00005205298,0.0002616911,0.0002997917,0.0001779186,0.0002149715],"domain_scores_gemma":[0.9986497,0.0003554419,0.00006790504,0.0006596677,0.000188726,0.00007860021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[9.162501e-7,0.0002615853,0.0001889131,0.00005709946,0.00002277404,1.241643e-7,0.0002142379,0.00004312377,0.001058065,0.4757425,0.02457133,0.4978394],"study_design_scores_gemma":[0.001211325,0.0003410731,0.01085958,0.000007677659,0.0000214988,0.000004536016,0.0004991673,0.1092061,0.01756552,0.1119768,0.7473975,0.00090931],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003713097,0.00006816242,0.9891726,0.001187312,0.00006322003,0.001260189,0.000002605024,0.0001844072,0.007690158],"genre_scores_gemma":[0.3122787,0.000007788688,0.6737307,0.002697015,0.0000999054,0.006194263,0.000008010855,0.00000791345,0.004975717],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7228262,"threshold_uncertainty_score":0.6978225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04118046887103981,"score_gpt":0.3718166637512564,"score_spread":0.3306361948802166,"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."}}