{"id":"W2385803373","doi":"","title":"Metric of image quality based on structural similarity","year":2007,"lang":"en","type":"article","venue":"Guangdian gongcheng","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Artificial intelligence; Image quality; Set partitioning in hierarchical trees; Computer science; Luminance; Image (mathematics); Computer vision; Pattern recognition (psychology); Distortion (music); Feature detection (computer vision); Similarity (geometry); Image compression; Set (abstract data type); Mathematics; Image processing","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":[],"consensus_categories":[],"category_scores_codex":[0.001614267,0.0001808824,0.0002734631,0.0004285112,0.00005531681,0.00001737965,0.000187157,0.0001594332,0.0001945337],"category_scores_gemma":[0.001218374,0.0001816916,0.0001275889,0.0006658928,0.00006837327,0.0001190489,0.00002060183,0.0003370279,0.00002325813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000137458,"about_ca_system_score_gemma":0.00001719436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003437417,"about_ca_topic_score_gemma":0.00001630766,"domain_scores_codex":[0.9986745,0.000129174,0.0004049667,0.0001829062,0.0002532704,0.0003551864],"domain_scores_gemma":[0.9985378,0.0008269305,0.00007361739,0.0003925709,0.00008315367,0.00008593767],"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":[0.0005476523,0.0001147757,0.04427367,0.001654782,0.000321888,0.0001382729,0.002178831,0.1965442,0.5974179,0.003927918,0.001689525,0.1511906],"study_design_scores_gemma":[0.0004795582,0.00006207171,0.2531058,0.00001537732,0.00001643141,0.000002451584,0.0002377338,0.01597684,0.7283838,0.001096533,0.0002946289,0.0003287947],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8879922,0.000119871,0.09787957,0.00002791522,0.0008091377,0.0001626799,0.00004720091,0.0005259582,0.01243547],"genre_scores_gemma":[0.8458341,0.000001919545,0.153955,0.00006608978,0.00008615842,0.000002971501,0.000007975482,0.00002657706,0.0000191653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2088321,"threshold_uncertainty_score":0.7409168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04001883460507018,"score_gpt":0.3246959976842536,"score_spread":0.2846771630791834,"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."}}