{"id":"W4235126022","doi":"10.32920/ryerson.14647569","title":"K-means clustering based tone-mapping operator for high dynamic range video","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Tone mapping; High dynamic range; Cluster analysis; Computer science; Luminance; Centroid; Artificial intelligence; Dynamic range; Computer vision; Range (aeronautics); Flicker; Frame (networking); Algorithm; Computer graphics (images); 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":[0.0006618784,0.0006801394,0.0006613038,0.0009650872,0.0007065514,0.0008843956,0.00127959,0.0007696262,0.003293391],"category_scores_gemma":[0.002636499,0.0002811894,0.000770931,0.0009408735,0.0005845735,0.001096641,0.0008910219,0.0011495,0.001214667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006741713,"about_ca_system_score_gemma":0.0007292934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003557235,"about_ca_topic_score_gemma":0.004144334,"domain_scores_codex":[0.9993156,0.00009547436,0.00004456872,0.0001879841,0.0002848303,0.00007158122],"domain_scores_gemma":[0.999185,0.0002184655,0.00006273202,0.0001482838,0.0003398361,0.00004565292],"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":[0.0005477265,0.0001567004,0.0009626815,0.0002048143,0.0000844876,0.0001342968,0.0003931241,0.082307,0.1273151,0.01256191,0.005779605,0.7695525],"study_design_scores_gemma":[0.00003263283,0.0001138253,0.00144047,0.00001362238,0.00002442124,0.0002603256,0.000112422,0.914373,0.0710296,0.007761744,0.004790056,0.00004785886],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01610011,0.00008624122,0.9818739,0.00004929775,0.00003392197,0.00005961809,0.00004073968,0.0009248989,0.0008313282],"genre_scores_gemma":[0.1199438,0.000118215,0.876767,0.00007777879,0.00003735969,0.0001150841,0.0001810962,0.0002440879,0.002515499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003557235,"threshold_uncertainty_score":0.0110175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036936154178004,"score_gpt":0.2909148078365107,"score_spread":0.2705454462947307,"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."}}