{"id":"W4403330974","doi":"10.1016/j.jvcir.2024.104302","title":"OODNet: A deep blind JPEG image compression deblocking network using out-of-distribution detection","year":2024,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Concordia University","funders":"","keywords":"Artificial intelligence; Computer vision; Deblocking filter; Computer science; Image compression; JPEG; Image (mathematics); Compression (physics); JPEG 2000; Distribution (mathematics); Pattern recognition (psychology); Mathematics; Image processing; Materials science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003183247,0.0005240234,0.0004426407,0.0003775047,0.0002041401,0.0003971342,0.001077164,0.0005180368,0.001533567],"category_scores_gemma":[0.0007792016,0.0001919548,0.0002121739,0.0002481466,0.0003188778,0.0007983361,0.0006523146,0.0008086359,0.0003829362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005263566,"about_ca_system_score_gemma":0.0006089224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004078981,"about_ca_topic_score_gemma":0.007394109,"domain_scores_codex":[0.9998702,0.00001320161,0.000007218306,0.00002497637,0.00005937619,0.00002504784],"domain_scores_gemma":[0.9998307,0.00004304353,0.00002396949,0.00002618991,0.0000579749,0.00001812654],"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":[0.0006467418,0.0002782845,0.001812411,0.0001681633,0.0001012121,0.0002106816,0.00006070756,0.1052591,0.06291435,0.004845531,0.009434584,0.8142682],"study_design_scores_gemma":[0.00003306507,0.0001531637,0.0004208289,0.0000170879,0.0000203461,0.0001173869,0.00000992854,0.9675513,0.02790322,0.001033542,0.002723576,0.00001654884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08146204,0.001923473,0.9072021,0.0003300726,0.0002746366,0.0001625107,0.0003047413,0.00318299,0.005157343],"genre_scores_gemma":[0.6345519,0.001009387,0.3458603,0.0006823271,0.00008549876,0.0001477937,0.001081966,0.0001442561,0.01643654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004078981,"threshold_uncertainty_score":0.008110464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0448230669955311,"score_gpt":0.4093659896502181,"score_spread":0.364542922654687,"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."}}