{"id":"W2154209128","doi":"10.1109/ccece.2004.1345268","title":"Rate-distortion optimization of spatial filters for motion-compensated video coding","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Codebook; Motion compensation; Quarter-pixel motion; Computer science; Computer vision; Artificial intelligence; Motion estimation; Rate–distortion optimization; Motion vector; Distortion (music); Coding (social sciences); Block-matching algorithm; Interpolation (computer graphics); Algorithm; Mathematics; Motion (physics); Image (mathematics); Bandwidth (computing); Video processing; Telecommunications; Video tracking","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":[0.0001627944,0.00008804189,0.0001292041,0.0001312331,0.0001096338,0.00004877465,0.0004107276,0.00006349593,0.0000124075],"category_scores_gemma":[0.000123381,0.00007755478,0.00005736441,0.0002095841,0.00003303732,0.0002759636,0.00009333225,0.00004676694,0.000003536872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005096292,"about_ca_system_score_gemma":0.00003213217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009894535,"about_ca_topic_score_gemma":0.000007686615,"domain_scores_codex":[0.999248,0.00001977159,0.0002404132,0.00023493,0.0001179548,0.0001388875],"domain_scores_gemma":[0.9993441,0.00005968147,0.0001358122,0.0003038669,0.0001300553,0.00002648023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003101546,0.0001140868,0.000160898,0.00004206094,0.00002030577,0.000001427934,0.0002389219,0.8283653,0.02123277,0.08857765,0.0005003047,0.06071524],"study_design_scores_gemma":[0.0008748582,0.000146775,0.0004620942,0.0000631448,0.000006108506,0.000002403572,0.00004279843,0.6016702,0.3880485,0.008429773,0.00009653336,0.0001567395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00792166,0.00001365339,0.9899871,0.0008754691,0.0002816338,0.0001790478,0.000001715307,0.0005072723,0.0002324812],"genre_scores_gemma":[0.8429443,0.000007392858,0.1568973,0.00005492804,0.00001231209,0.0000203644,0.000006537674,0.000004582728,0.00005226255],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8350226,"threshold_uncertainty_score":0.3162592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02143397056733009,"score_gpt":0.2425318390063733,"score_spread":0.2210978684390432,"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."}}