{"id":"W2584629362","doi":"10.1109/icecs.2016.7841297","title":"Evaluation of chroma subsampling for high dynamic range video compression","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Telus (Canada); University of British Columbia","funders":"","keywords":"Gamut; Computer science; Upsampling; Codec; High dynamic range; Data compression; Computer vision; Artificial intelligence; Coding (social sciences); Range (aeronautics); Pipeline transport; Dynamic range; Computer graphics (images); Computer hardware; Mathematics; Statistics","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":[0.0007946278,0.0004482246,0.0002715678,0.0007355718,0.0001880357,0.0004874908,0.0003701949,0.0003579315,0.001527907],"category_scores_gemma":[0.003265923,0.0001036399,0.0002264039,0.0005505135,0.0002579444,0.0004843063,0.0002072075,0.0001860612,0.0001897547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003292462,"about_ca_system_score_gemma":0.0002339423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002239511,"about_ca_topic_score_gemma":0.001845923,"domain_scores_codex":[0.9995168,0.00008357059,0.0000258216,0.00005071046,0.0002727914,0.00005023422],"domain_scores_gemma":[0.9982364,0.0009905061,0.0001312513,0.0001507664,0.0004220905,0.00006896826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005909008,0.0007927293,0.007494728,0.0004840123,0.0001763935,0.0004028408,0.0001416781,0.06719892,0.618764,0.001384729,0.0007964806,0.2964545],"study_design_scores_gemma":[0.0001477508,0.004606945,0.03690088,0.00004622737,0.0002239587,0.0007705371,0.0001233796,0.3288686,0.625348,0.0003992728,0.002500755,0.00006373017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9400901,0.001460431,0.05418193,0.00008308774,0.00005173027,0.00008946317,0.0001425034,0.0005410484,0.003359655],"genre_scores_gemma":[0.9615664,0.0005349115,0.0363837,0.00002864541,0.00001850448,0.0000202694,0.0002004156,0.00006452548,0.001182675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002239511,"threshold_uncertainty_score":0.005111337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03879591154560365,"score_gpt":0.32859451771036,"score_spread":0.2897986061647563,"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."}}