{"id":"W2575196123","doi":"10.1109/tip.2017.2651387","title":"Asymmetrically Compressed Stereoscopic 3D Videos: Quality Assessment and Rate-Distortion Performance Evaluation","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Stereoscopy; Coding (social sciences); Artificial intelligence; Computer vision; Quantization (signal processing); Multiview Video Coding; Data compression; Video quality; Motion compensation; Video compression picture types; Image quality; Video processing; Video tracking; Mathematics; Image (mathematics)","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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00203313,0.0002428368,0.0002864634,0.0002070327,0.001882397,0.001903001,0.0006557369,0.00008590473,0.00003073605],"category_scores_gemma":[0.00004158757,0.0002366462,0.00006504406,0.000211943,0.0001549677,0.00395917,0.00001570786,0.0003530687,0.00001626681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002766283,"about_ca_system_score_gemma":0.0003500762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008356178,"about_ca_topic_score_gemma":0.0000189429,"domain_scores_codex":[0.9974072,0.0003739939,0.00051707,0.0006328734,0.0007408331,0.0003280471],"domain_scores_gemma":[0.99791,0.0001391412,0.0004611668,0.0009113329,0.0004545486,0.0001238125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002635898,0.0002823883,0.0003083244,0.0002074763,0.00002445836,0.000002975799,0.0004278936,0.0004681546,0.006445791,0.00005185235,0.000016607,0.9917377],"study_design_scores_gemma":[0.001799231,0.0002439064,0.10848,0.000245567,0.00008477251,0.000008251726,0.00009282372,0.8486603,0.03943162,0.000345594,0.0001121869,0.000495696],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06192716,0.00006618177,0.9348041,0.0008924807,0.0004328816,0.0003894491,0.000004773916,0.0001442557,0.001338742],"genre_scores_gemma":[0.9229137,0.00004391618,0.07652737,0.0002590555,0.00003924721,0.0001031306,0.000002599832,0.00001456902,0.00009641562],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9912421,"threshold_uncertainty_score":0.999417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08599878962848397,"score_gpt":0.3969420703703129,"score_spread":0.310943280741829,"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."}}