{"id":"W1570785179","doi":"10.1109/pacrim.2003.1235759","title":"Removal of DCT blocking artifacts using DC and AC filtering","year":2004,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Blocking (statistics); Discrete cosine transform; Blocking effect; Block (permutation group theory); DC bias; Algorithm; Computer science; Low frequency; Image (mathematics); Mathematics; Voltage; Artificial intelligence; Electrical engineering; Engineering; Telecommunications; Geometry","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":[0.0002638917,0.0005322344,0.00048253,0.000668616,0.0003216119,0.0005393717,0.0003252178,0.0004340128,0.002384438],"category_scores_gemma":[0.001169557,0.0002027326,0.0004535714,0.0005196027,0.0002438012,0.0004239579,0.0002816423,0.000537006,0.001195025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002039761,"about_ca_system_score_gemma":0.0004431375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002275691,"about_ca_topic_score_gemma":0.003099582,"domain_scores_codex":[0.9997674,0.00001680047,0.00001603843,0.00003400995,0.0001398311,0.00002593539],"domain_scores_gemma":[0.9994362,0.0001434601,0.00005207737,0.00007965972,0.0002599403,0.00002860081],"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":[0.0002298532,0.00006938098,0.001092894,0.0002010472,0.00003616463,0.0002225661,0.00008190778,0.007460695,0.534752,0.003590069,0.002149997,0.4501135],"study_design_scores_gemma":[0.0000557793,0.0003746212,0.006987348,0.00004583295,0.0001408286,0.001472191,0.00008877504,0.1826112,0.7546236,0.002715127,0.05083173,0.00005299979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08719253,0.001408395,0.8995166,0.0002362198,0.0003021115,0.000102659,0.000165281,0.001912312,0.009163906],"genre_scores_gemma":[0.2228713,0.001702494,0.7610194,0.0002152298,0.0002073227,0.00006975936,0.0004830807,0.0003414033,0.01309012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002384438,"threshold_uncertainty_score":0.007976711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.048450939909817,"score_gpt":0.2998158406333563,"score_spread":0.2513649007235392,"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."}}