{"id":"W2081715705","doi":"10.1109/ivmspw.2013.6611930","title":"3D video quality metric for 3D video compression","year":2013,"lang":"en","type":"article","venue":"","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Codec; Computer science; Encoder; Metric (unit); Artificial intelligence; Mean opinion score; Video quality; Computer vision; ENCODE; Data compression; View synthesis; Multiview Video Coding; Encoding (memory); Video processing; Video tracking; Rendering (computer graphics)","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":[0.001068645,0.0007441626,0.0005593995,0.00218075,0.0002504992,0.001074981,0.000669471,0.0006382321,0.003540921],"category_scores_gemma":[0.004273886,0.0001399431,0.0005207892,0.001837665,0.0003408705,0.0007681915,0.0007288373,0.0004886574,0.001216112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007738814,"about_ca_system_score_gemma":0.0004309957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001658642,"about_ca_topic_score_gemma":0.001510744,"domain_scores_codex":[0.9975562,0.0002976398,0.000170307,0.0001555527,0.001764918,0.00005540604],"domain_scores_gemma":[0.9981335,0.0002955384,0.0002189761,0.0001775708,0.0011326,0.00004188085],"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":[0.0007025123,0.0001720733,0.007247461,0.001154831,0.0002022519,0.0004024396,0.0002190899,0.0475223,0.3074319,0.01379346,0.01262672,0.6085249],"study_design_scores_gemma":[0.00006769544,0.001049769,0.02947711,0.0002662324,0.0001580519,0.003095844,0.0001856033,0.5890161,0.3141589,0.004859965,0.05739993,0.0002646579],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05276243,0.005180164,0.9252559,0.0002678549,0.0004291321,0.00040153,0.002242792,0.001900718,0.01155938],"genre_scores_gemma":[0.5013052,0.002758442,0.4848123,0.0002483858,0.0001520608,0.0004968764,0.004291179,0.0003387024,0.005596836],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003540921,"threshold_uncertainty_score":0.01184559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05898639457497656,"score_gpt":0.3643352405380648,"score_spread":0.3053488459630883,"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."}}