{"id":"W4283658061","doi":"10.1002/sdtp.15669","title":"75‐2: The Effect of Chromatic Aberration Correction on Visually Lossless Compression","year":2022,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Advanced Optical Imaging Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Computer vision; Chromatic aberration; Distortion (music); Artificial intelligence; Chromatic scale; Color space; Lossless compression; Codec; Chromatic adaptation; Computer graphics (images); Optics; Data compression; Physics; Image (mathematics); Computer hardware; Telecommunications","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":[0.0003222195,0.0004230135,0.0002698818,0.0007507424,0.0003427329,0.0005322057,0.0004384036,0.0005083292,0.008709054],"category_scores_gemma":[0.003924193,0.00009838804,0.0001789937,0.0005935755,0.0003605961,0.0005908923,0.000337702,0.0003288673,0.0006117375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004731962,"about_ca_system_score_gemma":0.0004782895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007382002,"about_ca_topic_score_gemma":0.005301616,"domain_scores_codex":[0.9995918,0.00003762788,0.0000249352,0.00003585211,0.0002295269,0.00008031645],"domain_scores_gemma":[0.9971265,0.001749835,0.0001856542,0.0002117324,0.0005633976,0.0001628251],"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":[0.005213045,0.0007610944,0.009688276,0.000467021,0.0001223486,0.0009230091,0.0003190303,0.02958351,0.7574535,0.001752121,0.003576049,0.1901411],"study_design_scores_gemma":[0.000122821,0.004585725,0.0326751,0.00006230715,0.0001311112,0.001236166,0.0001217021,0.04563384,0.9098378,0.0002904946,0.005209983,0.00009298961],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859208,0.0004654209,0.00730723,0.0001105631,0.00008435245,0.00005622768,0.0003217718,0.0006497047,0.005083773],"genre_scores_gemma":[0.9916224,0.0001998745,0.004269476,0.00007242282,0.00001342006,0.00001980666,0.0003083336,0.0000976459,0.003396515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008709054,"threshold_uncertainty_score":0.02913463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004013475150969714,"score_gpt":0.2281143379363759,"score_spread":0.2241008627854062,"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."}}