{"id":"W2059071560","doi":"10.1117/12.877391","title":"Image quality of up-converted 2D video from frame-compatible 3D video","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Codec; Video compression picture types; PEVQ; Multiview Video Coding; Video quality; High-definition video; Computer vision; Subjective video quality; Smacker video; Image quality; Video tracking; Broadcasting (networking); Video processing; High definition; Artificial intelligence; MPEG-2; Video capture; Reference frame; Frame (networking); Computer graphics (images); Telecommunications; Image (mathematics); 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":[0.0001820839,0.0001986091,0.0001294871,0.0004419591,0.00008200233,0.0003726439,0.0001763941,0.0002432271,0.003509677],"category_scores_gemma":[0.0009417179,0.0001140931,0.000128938,0.0003268923,0.0002082573,0.0003489634,0.0002243937,0.0003108306,0.0002122768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002110253,"about_ca_system_score_gemma":0.00008890323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001584699,"about_ca_topic_score_gemma":0.0008578721,"domain_scores_codex":[0.9998325,0.0000193395,0.000009006149,0.0000242994,0.00009521042,0.00001951856],"domain_scores_gemma":[0.99938,0.0001775213,0.00009128261,0.00003253931,0.0002713944,0.00004722744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007146864,0.00005911872,0.001923618,0.0001940178,0.00001796461,0.0003173819,0.0001551968,0.001271134,0.9741762,0.0002366666,0.000184209,0.02074971],"study_design_scores_gemma":[0.00005571407,0.001756007,0.1495268,0.00008805269,0.000116559,0.001837363,0.0004899842,0.01439461,0.8285824,0.0002466058,0.002817419,0.00008847103],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929697,0.0004466691,0.004114422,0.0000253886,0.00002143627,0.00001801294,0.0001836046,0.00003812703,0.002182703],"genre_scores_gemma":[0.9934719,0.0004806267,0.003911294,0.00004332134,0.000008583873,0.00001296843,0.0003243215,0.00002823472,0.001718881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003509677,"threshold_uncertainty_score":0.01174104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03061688983947428,"score_gpt":0.2744859346246979,"score_spread":0.2438690447852236,"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."}}