{"id":"W1976478698","doi":"10.1109/tip.2012.2221725","title":"Objective Quality Assessment of Tone-Mapped Images","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":630,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Tone mapping; Computer science; Naturalness; Artificial intelligence; Image quality; Measure (data warehouse); Visualization; Computer vision; Tone (literature); High dynamic range; Pattern recognition (psychology); Ranking (information retrieval); Image (mathematics); Dynamic range; Data mining","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":[0.001578209,0.0004949485,0.0004278724,0.001282828,0.0001978751,0.001085328,0.0004215118,0.0004885682,0.002259039],"category_scores_gemma":[0.006056968,0.000170945,0.0003250662,0.0004797749,0.000408703,0.001165098,0.0007676742,0.0003567978,0.0003324049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002471089,"about_ca_system_score_gemma":0.0001936732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004404282,"about_ca_topic_score_gemma":0.0004826753,"domain_scores_codex":[0.9991258,0.0001537145,0.00006845202,0.0001406329,0.0004667895,0.00004460038],"domain_scores_gemma":[0.9968003,0.0009402725,0.0004715545,0.0003007422,0.001359228,0.0001278855],"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":[0.001438916,0.0002128999,0.01377866,0.0009286684,0.0002078632,0.0003017498,0.0004952815,0.03253251,0.5370904,0.003936972,0.00155037,0.4075257],"study_design_scores_gemma":[0.0001299158,0.001956678,0.0725746,0.000120481,0.0003186391,0.001901702,0.000521804,0.4553725,0.4558238,0.005314711,0.005719244,0.000245859],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3234554,0.000677822,0.6718777,0.0001176401,0.00006702661,0.0002056365,0.000277426,0.0006096725,0.002711672],"genre_scores_gemma":[0.7838424,0.0005900195,0.212646,0.0000651762,0.00006699566,0.0001042862,0.0004014436,0.0001695583,0.002114167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002259039,"threshold_uncertainty_score":0.008346498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03168889954894181,"score_gpt":0.3745780248808502,"score_spread":0.3428891253319084,"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."}}