{"id":"W2806707183","doi":"10.1002/sdtp.12106","title":"85‐1: Visually Lossless Compression of High Dynamic Range Images: A Large‐Scale Evaluation","year":2018,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Qualcomm (Canada); York University","funders":"","keywords":"Lossless compression; High dynamic range; Computer science; Flicker; Dynamic range compression; Codec; Compression (physics); Dynamic range; Computer vision; Scale (ratio); Image compression; Data compression; Artificial intelligence; Computer graphics (images); Computer hardware; Image (mathematics); Image processing; Materials science; Telecommunications; Cartography; Geography","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":[0.001105278,0.000279784,0.000481679,0.0002143797,0.0001239751,0.00004863243,0.001520853,0.0002072084,0.00007824528],"category_scores_gemma":[0.0001069511,0.0002535378,0.0001702446,0.0005739277,0.0005732344,0.0005353306,0.000672709,0.0002160682,0.00002057594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001796505,"about_ca_system_score_gemma":0.0001265434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004790761,"about_ca_topic_score_gemma":0.00005671909,"domain_scores_codex":[0.9968523,0.0002018051,0.0007172833,0.0006486698,0.001134586,0.0004452966],"domain_scores_gemma":[0.9975437,0.0001287589,0.0004498994,0.001219732,0.0005553864,0.0001025485],"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":[0.00005176715,0.0003876701,0.0002964601,0.00006196983,0.00001539036,0.000002264727,0.0001948028,0.0000133873,0.996268,0.001343779,0.0003992789,0.000965195],"study_design_scores_gemma":[0.001503004,0.001098872,0.02069266,0.0004371892,0.00006679385,0.00001035286,0.00004073733,0.0018517,0.9724073,0.0009355739,0.0005040513,0.0004517729],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4694375,0.0005502692,0.143953,0.004705056,0.001540838,0.004682528,0.0001197717,0.002840715,0.3721703],"genre_scores_gemma":[0.9882225,0.00006169948,0.01126688,0.000128814,0.00004917757,0.00009249042,0.00001874537,0.00002802705,0.0001316769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.518785,"threshold_uncertainty_score":0.9999917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007825180964762707,"score_gpt":0.2883745563859382,"score_spread":0.2805493754211755,"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."}}