{"id":"W1520563248","doi":"10.1109/tip.2015.2456419","title":"Towards a Full-Reference Quality Assessment for Color Images Using Directional Statistics","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Hue; Artificial intelligence; Achromatic lens; Weighting; Computer science; Computer vision; Metric (unit); Chromatic scale; Lightness; Pattern recognition (psychology); Color balance; Color space; Color difference; Similarity (geometry); Color image; Mathematics; Image processing; Enhanced Data Rates for GSM Evolution; Image (mathematics); Optics","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":[0.00257256,0.0008320616,0.0008609314,0.002191249,0.0003062019,0.002007751,0.001750642,0.0007469819,0.0008987602],"category_scores_gemma":[0.009139367,0.0003045378,0.0007936618,0.001638107,0.0008439163,0.002321448,0.001358049,0.001008785,0.0006233372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008510172,"about_ca_system_score_gemma":0.0009087342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002890777,"about_ca_topic_score_gemma":0.002808951,"domain_scores_codex":[0.9976908,0.0005794896,0.0001269148,0.0003461659,0.001187532,0.00006909022],"domain_scores_gemma":[0.9960448,0.001056155,0.0006433469,0.0007257863,0.001398882,0.0001312176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004098834,0.000259403,0.007430453,0.0003065768,0.0001841809,0.0001485984,0.0002705523,0.3731107,0.08660305,0.0410413,0.002732072,0.4875033],"study_design_scores_gemma":[0.000008209081,0.0001443497,0.001707495,0.00001676605,0.00001722747,0.00009847015,0.00002909987,0.9826303,0.007944195,0.006269212,0.001094565,0.00004004977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01022764,0.0001369178,0.9888116,0.00004209058,0.00001145497,0.00002645948,0.00004807767,0.000255555,0.0004401921],"genre_scores_gemma":[0.3938653,0.0004381209,0.6037112,0.00008662851,0.00006310845,0.0001468368,0.0003819524,0.0001851436,0.001121707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002890777,"threshold_uncertainty_score":0.01360518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1547351181846662,"score_gpt":0.4242709777059488,"score_spread":0.2695358595212827,"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."}}