{"id":"W3098560717","doi":"","title":"DipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs","year":2017,"lang":"en","type":"article","venue":"UTS ePRESS (University of Technology Sydney)","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":168,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Image quality; Computer science; Artificial intelligence; Robustness (evolution); Machine learning; Pairwise comparison; Ground truth; Pixel; Quality (philosophy); Learning to rank; Pattern recognition (psychology); Image (mathematics); Computer vision; Ranking (information retrieval)","routes":{"ca_aff":true,"ca_fund":true,"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","sts"],"consensus_categories":[],"category_scores_codex":[0.0008639685,0.0002960292,0.0005846878,0.0004431373,0.001470257,0.0002793754,0.004590281,0.0003067847,0.0001242198],"category_scores_gemma":[0.0002507317,0.0003396231,0.0001914505,0.0003005382,0.000874483,0.001926089,0.003307735,0.0006440026,0.0001341486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001384386,"about_ca_system_score_gemma":0.0001359374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001924862,"about_ca_topic_score_gemma":0.0001235222,"domain_scores_codex":[0.9974523,0.0002078434,0.0003025467,0.0009191501,0.0004950061,0.0006232135],"domain_scores_gemma":[0.9963098,0.00009538238,0.0005734332,0.002520968,0.0003011902,0.0001992444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004161098,0.003764012,0.068588,0.0009649431,0.001059889,0.001232203,0.0103866,0.00007601951,0.3384616,0.2642353,0.1116548,0.1991605],"study_design_scores_gemma":[0.01980136,0.003513031,0.3643354,0.0008054547,0.0007004988,0.00009839862,0.04137758,0.02816174,0.1630415,0.05481063,0.3168733,0.006481145],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1873866,0.00006880094,0.7610303,0.03715406,0.0002570674,0.0004885215,0.00004620579,0.0005982179,0.01297022],"genre_scores_gemma":[0.8598685,0.00005230743,0.1355867,0.000119584,0.00001793654,0.000004117001,0.00001508632,0.00001734721,0.004318417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6724819,"threshold_uncertainty_score":0.9999056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02829791564943497,"score_gpt":0.3132342590439317,"score_spread":0.2849363433944967,"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."}}