{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001125833,0.001094877,0.0006473532,0.002614422,0.0002933301,0.001450271,0.001074057,0.0009363777,0.002002046],"category_scores_gemma":[0.005121598,0.0002244337,0.0005447309,0.001361521,0.0003305054,0.001630176,0.001105129,0.0006306421,0.001093296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008331687,"about_ca_system_score_gemma":0.0004091607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002848789,"about_ca_topic_score_gemma":0.001883524,"domain_scores_codex":[0.9979564,0.0003431193,0.0001128264,0.0001908825,0.001331359,0.00006544183],"domain_scores_gemma":[0.997856,0.0002891442,0.0002461916,0.000240626,0.001283242,0.00008471745],"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":[0.000585694,0.0001167518,0.00587381,0.0008765926,0.0001924172,0.0003149519,0.0001884522,0.09209237,0.1493589,0.01236768,0.008980773,0.7290516],"study_design_scores_gemma":[0.00004436512,0.0005804964,0.009040494,0.0001774302,0.0001378443,0.001116653,0.0001336111,0.8779614,0.07150686,0.005208564,0.03393817,0.0001539803],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01598904,0.003414825,0.9756874,0.0001359497,0.0001486409,0.0001502372,0.0004536506,0.001214364,0.002805952],"genre_scores_gemma":[0.4233479,0.002781761,0.567901,0.0001928371,0.0002192803,0.0002903871,0.001704194,0.0003543217,0.003208362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002848789,"threshold_uncertainty_score":0.006697536,"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."}}