{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00160638,0.000555138,0.0007731139,0.0006044756,0.000456358,0.0003725574,0.003335947,0.0005002263,0.00007840516],"category_scores_gemma":[0.0002474995,0.0006028858,0.0004997242,0.001108884,0.0002033059,0.0008888908,0.00418777,0.0006474178,0.0001847799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004534358,"about_ca_system_score_gemma":0.0004267843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005558113,"about_ca_topic_score_gemma":0.00005512553,"domain_scores_codex":[0.9956564,0.0006593513,0.0005987113,0.002068828,0.0003044113,0.0007122976],"domain_scores_gemma":[0.9948392,0.000799552,0.0007429154,0.002661808,0.0006512226,0.0003053376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001154682,0.003751979,0.01118404,0.00480558,0.002095268,0.0007722866,0.003946214,0.09921984,0.0009678802,0.7300299,0.07655992,0.06551238],"study_design_scores_gemma":[0.002297105,0.0003515443,0.004452121,0.0003933446,0.0002553862,0.000005400131,0.0001344183,0.8798811,0.001512626,0.07669687,0.03224274,0.001777299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02116811,0.0001375865,0.9725549,0.0002708311,0.001567266,0.0008654332,0.00006794057,0.0004159833,0.00295196],"genre_scores_gemma":[0.9240749,0.0001252346,0.0720029,0.0006574531,0.0003714554,0.00000953334,0.00006911422,0.00003737767,0.002652044],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9029068,"threshold_uncertainty_score":0.9996423,"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."}}