{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002213272,0.0001792865,0.000296786,0.00009299487,0.0001310078,0.000009280734,0.0004037632,0.0000824871,0.00002295735],"category_scores_gemma":[0.0001698426,0.0001300274,0.0001051552,0.0003283341,0.0002311242,0.00006935067,0.0001695448,0.0004632411,0.0000035471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001751811,"about_ca_system_score_gemma":0.000006472684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006197999,"about_ca_topic_score_gemma":0.000001591202,"domain_scores_codex":[0.9988208,0.00007838394,0.0003446218,0.0001851558,0.0003766748,0.0001943406],"domain_scores_gemma":[0.9988264,0.0005673773,0.0001084225,0.0004495883,0.00001957875,0.00002861421],"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":[0.00004963512,0.00004128248,0.0003436219,0.00006985173,0.00001593525,0.000001462106,0.00002626929,0.1673198,0.8309259,0.0002783196,0.0003399281,0.0005879789],"study_design_scores_gemma":[0.0006289166,0.002096364,0.007610769,0.0002133891,0.00006915125,0.00002136935,0.0001085817,0.006674234,0.9812943,0.000131204,0.0008464243,0.0003053115],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.874225,0.0002449853,0.0002850467,0.0007218824,0.001307207,0.001149871,0.00001776003,0.002102486,0.1199458],"genre_scores_gemma":[0.9996394,0.00003003242,0.00008320505,0.00001985725,0.00001532231,0.0001330374,0.000009722539,0.00003283428,0.00003662517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1606455,"threshold_uncertainty_score":0.5302362,"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."}}