{"id":"W3112328948","doi":"10.18280/ts.370507","title":"Evaluation of Textural Degradation in Compressed Medical and Biometric Images by Analyzing Image Texture Features and Edges","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche","keywords":"Computer science; Biometrics; Artificial intelligence; Computer vision; Texture compression; Image compression; Texture (cosmology); Wavelet; Image quality; Image texture; Process (computing); Image (mathematics); Data compression; Enhanced Data Rates for GSM Evolution; Wavelet transform; Pattern recognition (psychology); Image processing","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":[0.0006236189,0.0002258322,0.0001931763,0.001741418,0.0001283575,0.0003620143,0.0001624762,0.0003411485,0.001061304],"category_scores_gemma":[0.00243777,0.00009954902,0.0001589374,0.0006382838,0.0002988655,0.0004357382,0.0001996528,0.0001924296,0.0001294805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001553921,"about_ca_system_score_gemma":0.00009616769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007184158,"about_ca_topic_score_gemma":0.0007039687,"domain_scores_codex":[0.9997264,0.00004050986,0.00002240724,0.0000292284,0.000159991,0.00002155015],"domain_scores_gemma":[0.9982013,0.0006503331,0.0003384473,0.0001195472,0.000604713,0.00008563345],"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":[0.001635596,0.0002061769,0.02494618,0.0003676757,0.00007337685,0.0005774058,0.0003593697,0.00747039,0.8489393,0.000456097,0.0003183236,0.11465],"study_design_scores_gemma":[0.00004542148,0.002202024,0.2813727,0.00005736881,0.0001810075,0.003694052,0.0005598461,0.1348402,0.5747797,0.0005585246,0.001637321,0.00007185457],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9656668,0.0005752837,0.03227067,0.00005137256,0.00001975968,0.00004208555,0.0001429293,0.0001620814,0.001069042],"genre_scores_gemma":[0.9839643,0.0003749437,0.01473153,0.0000251585,0.00001271743,0.00001585256,0.0001782276,0.0000320991,0.0006652313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001741418,"threshold_uncertainty_score":0.003550351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04001091163978707,"score_gpt":0.3151377247852076,"score_spread":0.2751268131454205,"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."}}