{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003062116,0.0005000562,0.000362517,0.0002545631,0.0002012234,0.0003732768,0.0007249626,0.0004367326,0.001930695],"category_scores_gemma":[0.0006275728,0.0001487743,0.000281269,0.0001884771,0.0003649515,0.0007256403,0.0005178627,0.0006944749,0.0003413365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002929572,"about_ca_system_score_gemma":0.0003371017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001567918,"about_ca_topic_score_gemma":0.003121278,"domain_scores_codex":[0.9998832,0.00001411788,0.000006896219,0.00002711604,0.00004845527,0.0000201982],"domain_scores_gemma":[0.9998147,0.00003954262,0.00003073614,0.00004159917,0.00005623179,0.00001721898],"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":[0.0008610333,0.0002063124,0.001093522,0.000187356,0.00007538672,0.0002960147,0.0001357122,0.08652757,0.2532352,0.006904567,0.002583871,0.6478934],"study_design_scores_gemma":[0.00002200571,0.0003013139,0.0007769296,0.00002056603,0.00003530057,0.0002408588,0.00002136394,0.8427691,0.1510942,0.001901397,0.002791658,0.00002536613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.102847,0.0005977912,0.8912438,0.0001340498,0.00008994476,0.00006220234,0.00006667592,0.001551652,0.003406854],"genre_scores_gemma":[0.7286078,0.0002903395,0.2627937,0.0001648603,0.00003257413,0.00003837567,0.0001631082,0.0001209183,0.007788231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001930695,"threshold_uncertainty_score":0.006458759,"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."}}