{"id":"W1480639945","doi":"10.1109/iscas.1994.408901","title":"Adaptive video coding using mixed-domain filter banks having optimal-shaped subbands","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Filter bank; Coding (social sciences); Computer science; Filter (signal processing); Sub-band coding; Adaptive filter; Algorithm; Speech recognition; Computer vision; Mathematics; Speech coding; Statistics","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":[0.0003324556,0.0004382486,0.000276752,0.0004003755,0.0001654171,0.0004642951,0.0004521085,0.0006124711,0.00148138],"category_scores_gemma":[0.000845728,0.0002049674,0.0002980246,0.0003570243,0.0003215273,0.0008508539,0.0003415107,0.0004764028,0.0005839446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002314173,"about_ca_system_score_gemma":0.0002259968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004764884,"about_ca_topic_score_gemma":0.001095454,"domain_scores_codex":[0.9998217,0.00005120765,0.00001169846,0.00003102151,0.00006806678,0.00001629228],"domain_scores_gemma":[0.9997345,0.0001133171,0.00002297037,0.00003798329,0.00007756294,0.00001364403],"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":[0.0005644029,0.0001088644,0.00066502,0.0002407521,0.0000916903,0.0001628139,0.0001004915,0.05159607,0.4145182,0.04655512,0.002459749,0.4829368],"study_design_scores_gemma":[0.00004171157,0.0001811403,0.0005511367,0.00003833389,0.00005081516,0.0003122883,0.00002062918,0.8357306,0.1469349,0.00661494,0.009488296,0.00003520757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01574626,0.0003466948,0.9822918,0.00006521276,0.00004649186,0.00001692275,0.00002044231,0.0001542134,0.001311873],"genre_scores_gemma":[0.1808254,0.0006632831,0.8145474,0.0001313865,0.00007027369,0.00006168717,0.00009605971,0.00005103094,0.003553491],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00148138,"threshold_uncertainty_score":0.004955709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06622097736275302,"score_gpt":0.2794001788747967,"score_spread":0.2131792015120437,"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."}}