{"id":"W2146022893","doi":"10.1109/ism.2008.74","title":"Deblocking of Block-Transform Compressed Images Using Phase-Adaptive Shifted Thresholding","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deblocking filter; Artificial intelligence; Computer science; Quantization (signal processing); Thresholding; Computer vision; Image compression; Transform coding; Peak signal-to-noise ratio; Color Cell Compression; Data compression; Uncompressed video; Pixel; Chrominance; Discrete cosine transform; Algorithm; Pattern recognition (psychology); Image processing; Image (mathematics); Luminance","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002714531,0.0002967295,0.0003362027,0.0006852644,0.0002145371,0.0003807264,0.0003731679,0.0003492428,0.001229378],"category_scores_gemma":[0.001353681,0.0001566734,0.0002088401,0.0005673295,0.0002927302,0.0007029424,0.0003323672,0.000354247,0.0002698368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002024012,"about_ca_system_score_gemma":0.0002606167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006133353,"about_ca_topic_score_gemma":0.001157714,"domain_scores_codex":[0.9998457,0.00001923084,0.0000101953,0.00002688298,0.00008563836,0.00001232484],"domain_scores_gemma":[0.9996074,0.0001505888,0.00006012031,0.00006226473,0.0001032237,0.00001637888],"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":[0.0003587796,0.00006737233,0.0008509711,0.000162667,0.00003093568,0.000228814,0.0001641366,0.01615668,0.475367,0.003992973,0.0008304838,0.5017892],"study_design_scores_gemma":[0.00004873587,0.0003249241,0.003615133,0.0000362973,0.00005737261,0.001697439,0.0001088041,0.4765529,0.5079749,0.002947898,0.006589727,0.00004588391],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.119534,0.0006258716,0.8769559,0.0001390845,0.00006759696,0.00007610083,0.0000436784,0.0006625467,0.001895282],"genre_scores_gemma":[0.3514145,0.0006698745,0.6451981,0.00006735249,0.00004413745,0.00003860712,0.0001276239,0.00008980919,0.002349942],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001229378,"threshold_uncertainty_score":0.004112661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05837082683400702,"score_gpt":0.3204563209716003,"score_spread":0.2620854941375933,"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."}}