{"id":"W2744611728","doi":"10.23977/jeis.2016.11003","title":"A no-reference image quality measure based on CPBD and noise","year":2016,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Measure (data warehouse); Noise (video); Computer science; Image quality; Metric (unit); Artificial intelligence; Computer vision; Image (mathematics); Enhanced Data Rates for GSM Evolution; Quality (philosophy); Image noise; Pattern recognition (psychology); Mathematics; Data mining; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001412874,0.0009979474,0.0007676554,0.002975913,0.0003556049,0.001532047,0.0008756099,0.001053575,0.001878238],"category_scores_gemma":[0.006422967,0.0002408444,0.000596491,0.001662569,0.000774974,0.001993257,0.001122888,0.0006026625,0.0005718864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006686641,"about_ca_system_score_gemma":0.0005367326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001342552,"about_ca_topic_score_gemma":0.0009157922,"domain_scores_codex":[0.9974282,0.0003451165,0.0001425867,0.0003620868,0.001612324,0.0001096692],"domain_scores_gemma":[0.9963377,0.0006842828,0.0004480289,0.0003997313,0.001979972,0.0001504199],"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":[0.00167752,0.0004011609,0.01557913,0.001929357,0.0003566178,0.0006036991,0.0003416653,0.04230198,0.2923815,0.01237903,0.003791773,0.6282566],"study_design_scores_gemma":[0.0001226831,0.002965682,0.07668024,0.0002921478,0.000576447,0.006182604,0.0004545074,0.5772957,0.3086861,0.01054661,0.01570122,0.0004960439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06656526,0.002473272,0.924806,0.000114958,0.0001478052,0.000145542,0.0002514755,0.0007586076,0.004737137],"genre_scores_gemma":[0.7239757,0.001628363,0.2702981,0.0001402409,0.0001373207,0.0001757705,0.0007968483,0.0002185345,0.00262927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002975913,"threshold_uncertainty_score":0.007472038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02572053596724407,"score_gpt":0.2822281448576235,"score_spread":0.2565076088903795,"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."}}