{"id":"W1978606138","doi":"10.1109/icip.2010.5653283","title":"Visually-favorable tone-mapping with high compression performance","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Tone mapping; Computer science; Tone (literature); Data compression; Coding (social sciences); Image compression; Artificial intelligence; Computer vision; Layer (electronics); Compression (physics); Image quality; Speech recognition; Image (mathematics); Mathematics; Image processing; High dynamic range","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":[],"consensus_categories":[],"category_scores_codex":[0.0002114968,0.000129991,0.0001182125,0.00008889611,0.000161972,0.0001665326,0.0007935307,0.00004720102,0.0001191194],"category_scores_gemma":[0.000006256668,0.00009336467,0.00001627071,0.0002778537,0.00004765774,0.001159847,0.0002796052,0.0002326631,0.0000778106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001494693,"about_ca_system_score_gemma":0.00004360493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004578596,"about_ca_topic_score_gemma":0.00001210326,"domain_scores_codex":[0.9989669,0.00001525365,0.0001429345,0.0003147553,0.0002821514,0.0002779715],"domain_scores_gemma":[0.9991388,0.00002450355,0.00006481917,0.0006191591,0.00009204803,0.00006069016],"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":[0.00001584581,0.0002085459,0.004897376,0.00005847232,0.00001772635,0.00001925029,0.0005201731,0.0000381954,0.829628,0.0631649,0.00774805,0.09368346],"study_design_scores_gemma":[0.0002815751,0.0001877069,0.005512019,0.00006581209,0.000001755147,0.00001775883,0.00001004014,0.04188229,0.9399428,0.0003397573,0.01147359,0.0002848686],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3416867,0.000003964456,0.640541,0.0002461017,0.0001716783,0.0001350445,1.104917e-7,0.0006929344,0.01652247],"genre_scores_gemma":[0.6531155,0.000004235209,0.3450847,0.0002354199,0.00003193966,0.0000177006,9.730597e-7,0.000006337966,0.001503228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3114288,"threshold_uncertainty_score":0.3807301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007996775351713194,"score_gpt":0.2489971923604925,"score_spread":0.2410004170087793,"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."}}