{"id":"W2920184167","doi":"10.1109/tcsii.2019.2903101","title":"Mantissa-Exponent-Based Tone Mapping for Wide Dynamic Range Image Sensors","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Tone mapping; Pixel; Computer science; High dynamic range; Dynamic range; Luminance; Histogram; Computer vision; Range (aeronautics); Image processing; Algorithm; Tone (literature); Image (mathematics); Exponent; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002371481,0.0005065646,0.0006348679,0.0003784231,0.000313154,0.0001590782,0.0003168605,0.0002057746,0.00009635475],"category_scores_gemma":[0.000007514647,0.0005759421,0.0003403962,0.0003151706,0.0000649744,0.0003441733,0.000001833612,0.0003829298,0.000244041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002466483,"about_ca_system_score_gemma":0.00004109347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001445904,"about_ca_topic_score_gemma":0.0000150649,"domain_scores_codex":[0.9974859,0.00007938432,0.0006474704,0.0006129285,0.0004246905,0.0007496192],"domain_scores_gemma":[0.9984799,0.0003091641,0.0001038621,0.0008027426,0.0001179339,0.0001863749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004476601,0.000191593,0.0000699583,0.001814605,0.0002771378,0.00003150994,0.001305454,0.5684958,0.4218174,0.00003067834,0.001708525,0.004212605],"study_design_scores_gemma":[0.008398714,0.0003219288,0.0007977794,0.002749331,0.0002994223,0.0001617129,0.001743628,0.7704667,0.1492483,0.00001964732,0.0628826,0.002910158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3898731,0.0002772273,0.6024234,0.0001004931,0.003480502,0.001480509,0.0003236892,0.001090964,0.0009500732],"genre_scores_gemma":[0.9953033,0.00002924544,0.0004013285,0.00008171149,0.0001018432,0.0003197984,0.00002429695,0.0002128587,0.003525602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6054302,"threshold_uncertainty_score":0.9996692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059546011013872,"score_gpt":0.2231106813485847,"score_spread":0.212515221238446,"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."}}