{"id":"W3090149108","doi":"10.1109/icip40778.2020.9191030","title":"Development Of New Fractal And Non-Fractal Deep Residual Networks For Deblocking Of Jpeg Decompressed Images","year":2020,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Deblocking filter; JPEG; Residual; Computer science; Artificial intelligence; Block (permutation group theory); Convolutional neural network; Transform coding; Computer vision; Compression artifact; Blocking (statistics); Image compression; Pattern recognition (psychology); Image processing; Data compression; Algorithm; Image (mathematics); Discrete cosine transform; Mathematics","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":[0.0003284638,0.00013315,0.0002839054,0.00005186979,0.00007960736,0.00007102267,0.0004299468,0.00006095671,0.00001067711],"category_scores_gemma":[0.0001077896,0.0001179371,0.00005343274,0.000181432,0.0000348342,0.0003070304,0.0002858966,0.00008961865,7.614619e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007397741,"about_ca_system_score_gemma":0.0001356929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000022128,"about_ca_topic_score_gemma":0.000006476605,"domain_scores_codex":[0.9988552,0.00003851269,0.0003955083,0.0002932911,0.000195355,0.000222171],"domain_scores_gemma":[0.9990954,0.0003386181,0.0001564919,0.000170946,0.0001039666,0.0001346207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002253528,0.00007989212,0.0008397363,0.0001599105,0.0001086566,0.00001500097,0.007776256,0.001502222,0.1634367,0.0005367557,0.003402111,0.8219174],"study_design_scores_gemma":[0.001709004,0.000193986,0.007270915,0.00006282392,0.0000226446,0.000007965592,0.0001286601,0.382138,0.60637,0.0005195172,0.001275694,0.0003007858],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05784343,0.0002375574,0.9409503,0.0003328026,0.00007491092,0.0001566394,6.435461e-7,0.0000344367,0.0003692597],"genre_scores_gemma":[0.416912,0.000003752519,0.5827738,0.00020299,0.00005957401,0.000002171986,0.000001269748,0.000006352174,0.00003802308],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8216166,"threshold_uncertainty_score":0.4809337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961745066394762,"score_gpt":0.2873016220637171,"score_spread":0.2576841713997695,"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."}}