{"id":"W2508520635","doi":"10.1109/icip.2016.7532487","title":"Objective quality assessment of tone-mapped videos","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Tone mapping; Naturalness; Computer science; High dynamic range; Tone (literature); Visualization; High fidelity; Fidelity; Perception; Artificial intelligence; Range (aeronautics); Quality (philosophy); Computer vision; Speech recognition; Dynamic range; Engineering","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":[0.001304811,0.0006218585,0.0003860152,0.001031951,0.0001298548,0.0009011852,0.0003998175,0.000456468,0.001854341],"category_scores_gemma":[0.006342569,0.0001581841,0.0002997289,0.0003994432,0.0003008859,0.0007916367,0.0005733955,0.0002894814,0.0003625878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002315164,"about_ca_system_score_gemma":0.0001710731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001050418,"about_ca_topic_score_gemma":0.0009736731,"domain_scores_codex":[0.9992191,0.0001782104,0.00005079712,0.0001374858,0.0003773794,0.00003706872],"domain_scores_gemma":[0.9970198,0.0009514413,0.000563101,0.0001884881,0.001156872,0.0001202034],"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":[0.0018605,0.0003764464,0.03070469,0.001748651,0.0003145906,0.0004968564,0.0008168733,0.05821167,0.423141,0.002434972,0.002352775,0.4775409],"study_design_scores_gemma":[0.0001260526,0.002271798,0.1246191,0.0002289793,0.0003088358,0.00168944,0.0005561467,0.6756801,0.186645,0.00293144,0.004725354,0.0002177983],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4408369,0.001388047,0.5529305,0.0001125869,0.00008181927,0.0003304532,0.0006653526,0.0006265567,0.003027836],"genre_scores_gemma":[0.904941,0.001105447,0.09023762,0.00006995816,0.00008520775,0.0001180184,0.0006161816,0.0001394142,0.00268715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001854341,"threshold_uncertainty_score":0.006900549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03137359623902838,"score_gpt":0.3851761088677536,"score_spread":0.3538025126287253,"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."}}