{"id":"W2952944171","doi":"10.48550/arxiv.1803.04629","title":"3D Video Quality Metric for 3D Video Compression","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Codec; Computer science; Metric (unit); Encoder; Mean opinion score; Artificial intelligence; ENCODE; Video quality; Computer vision; Encoding (memory); View synthesis; Data compression; Subjective video quality; Multiview Video Coding; Image quality; Video processing; Video tracking; Image (mathematics); 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":[0.00104466,0.0008308795,0.0005618139,0.002357475,0.0002512838,0.001202056,0.0006727345,0.0006987899,0.003580651],"category_scores_gemma":[0.004408294,0.0001452988,0.0005392606,0.002032747,0.0003723641,0.0008032041,0.0007835474,0.000548097,0.001298708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000803706,"about_ca_system_score_gemma":0.0004424664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001809736,"about_ca_topic_score_gemma":0.001580723,"domain_scores_codex":[0.9975634,0.000280733,0.0001612775,0.0001772595,0.001758904,0.00005848074],"domain_scores_gemma":[0.9982191,0.0002806171,0.0002056021,0.0001864118,0.001066691,0.00004159378],"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":[0.0006257693,0.0001769378,0.007321966,0.001188656,0.0002155065,0.000379436,0.0002236532,0.05331619,0.2654217,0.01534391,0.01291369,0.6428726],"study_design_scores_gemma":[0.00005913378,0.0009235016,0.02779898,0.0002974822,0.0001658521,0.00272611,0.0001919285,0.6112074,0.2943474,0.006269303,0.05576983,0.0002431077],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04890447,0.005391305,0.9282042,0.000262977,0.0004250692,0.0004091883,0.002393516,0.001928705,0.01208051],"genre_scores_gemma":[0.5126418,0.003192113,0.4722764,0.0002546145,0.0001728103,0.0004747654,0.004783577,0.000384261,0.00581955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003580651,"threshold_uncertainty_score":0.01197851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1589397656184239,"score_gpt":0.2821102124192298,"score_spread":0.123170446800806,"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."}}