{"id":"W3202420269","doi":"10.18280/ria.350406","title":"Blind Image Quality Assessment Using a CNN and Edge Distortion","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Image quality; Pattern recognition (psychology); Feature (linguistics); Pooling; Image (mathematics); Prewitt operator; Feature extraction; Distortion (music); Computer vision; Pyramid (geometry); Kernel (algebra); Enhanced Data Rates for GSM Evolution; Image processing; Edge detection; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009682661,0.0001773452,0.0002635734,0.00007620619,0.0002928622,0.0004433255,0.0003695314,0.00007082969,0.0001045134],"category_scores_gemma":[0.0001335543,0.0001877607,0.000103308,0.0005134225,0.0001007238,0.0007523847,0.0003988137,0.0002151184,0.00007447808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001399139,"about_ca_system_score_gemma":0.0002138962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008086562,"about_ca_topic_score_gemma":0.00002118043,"domain_scores_codex":[0.997815,0.0002874924,0.000595361,0.0006605478,0.0002917,0.0003498826],"domain_scores_gemma":[0.9984004,0.0001859895,0.0001773663,0.0008402205,0.0002589785,0.0001369793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000381481,0.001669171,0.003115901,0.0006652831,0.0001175311,0.0004548423,0.00892085,0.003328364,0.380326,0.3065566,0.0008061817,0.2940011],"study_design_scores_gemma":[0.0001916901,0.0001020949,0.002700545,0.0001503946,0.00003121516,0.0001310872,0.00229462,0.7299906,0.246514,0.01257752,0.004669859,0.0006464179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08763269,0.0003664376,0.9077242,0.001360127,0.0003730289,0.0001511067,0.000003639077,0.00007726299,0.002311542],"genre_scores_gemma":[0.891744,0.00009148369,0.1066867,0.0003437587,0.0001052075,0.00001428502,0.000009714844,0.00001224303,0.0009925698],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8041114,"threshold_uncertainty_score":0.7656659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1428013749629983,"score_gpt":0.4002284317670339,"score_spread":0.2574270568040355,"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."}}