{"id":"W2970009752","doi":"10.1109/icip.2019.8803374","title":"Deep Jpeg Image Deblocking Using Residual Maxout Units","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Deblocking filter; Computer science; Artificial intelligence; JPEG; Lossy compression; Computer vision; Image compression; Residual; Image restoration; Image (mathematics); Transform coding; Compression artifact; Image processing; Discrete cosine transform; Algorithm","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":[0.0002031956,0.0001567752,0.0001520908,0.0001356687,0.0001187414,0.0002867509,0.00097822,0.00005592019,0.00004208756],"category_scores_gemma":[0.00009146025,0.0001449508,0.00002387096,0.0006295574,0.00004370923,0.001687158,0.0006119078,0.0001629354,0.00009533694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006612034,"about_ca_system_score_gemma":0.0001023333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002336892,"about_ca_topic_score_gemma":0.000003909061,"domain_scores_codex":[0.9987122,0.00003892697,0.0001995133,0.0004307618,0.0002610173,0.0003575628],"domain_scores_gemma":[0.9988663,0.00006258753,0.0000939555,0.0006749961,0.000236555,0.00006565813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002641509,0.0001417193,0.005852639,0.0002173813,0.00004736518,0.0002611955,0.002350084,0.0007692986,0.7455165,0.06808443,0.001947088,0.1747859],"study_design_scores_gemma":[0.0002313078,0.00004563349,0.0001095445,0.00007828667,0.000004790065,0.0001086286,0.00005593328,0.8657522,0.1139182,0.0182618,0.001039854,0.0003937851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01172249,0.0001062803,0.9770057,0.0001838271,0.0001268861,0.00013733,2.287109e-7,0.001005849,0.009711381],"genre_scores_gemma":[0.1508985,0.000002980989,0.8480071,0.0003944233,0.00003292439,0.000002876713,6.676484e-7,0.00001758712,0.0006428918],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8649829,"threshold_uncertainty_score":0.5910923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232593519361722,"score_gpt":0.2844510914634047,"score_spread":0.2611917395272326,"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."}}