{"id":"W1606690892","doi":"10.1109/icip.2004.1419465","title":"Concealment of interpolation errors for low bit-rate motion compensated interpolation","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Interpolation (computer graphics); Computer science; Computer vision; Artificial intelligence; Motion estimation; Quarter-pixel motion; Motion compensation; Motion interpolation; Motion vector; Block (permutation group theory); Pixel; Frame (networking); Frame rate; Stairstep interpolation; Multivariate interpolation; Block-matching algorithm; Algorithm; Motion (physics); Mathematics; Bilinear interpolation; Image (mathematics); Video processing; 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":[0.0002335905,0.0001007001,0.0001434296,0.0001673668,0.00005681396,0.00004678567,0.0004392065,0.00006783944,0.00001820065],"category_scores_gemma":[0.00005561299,0.00008436565,0.00006041142,0.0002079833,0.00003737009,0.0004113167,0.0001409312,0.00006114087,0.00001316085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003599436,"about_ca_system_score_gemma":0.00001752154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002111529,"about_ca_topic_score_gemma":0.00000794943,"domain_scores_codex":[0.9991382,0.00004039875,0.00032756,0.0002337136,0.0001182744,0.000141827],"domain_scores_gemma":[0.9992313,0.00008654803,0.0001940961,0.0003204944,0.0001409839,0.00002662437],"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":[0.00007128506,0.0002339791,0.0009909959,0.00006351669,0.00004348565,4.339641e-7,0.001289011,0.002462144,0.2168592,0.2464223,0.004573308,0.5269904],"study_design_scores_gemma":[0.0003304884,0.00009924996,0.0005543361,0.00006039096,0.000003051379,0.000001030457,0.00009030585,0.8429625,0.1517592,0.003505459,0.0005401464,0.00009382969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08294308,0.0000252567,0.9137887,0.001850311,0.0001966668,0.0002269558,0.000001850926,0.0004155819,0.0005515946],"genre_scores_gemma":[0.9144239,0.00000395017,0.08523405,0.0001488745,0.00001842246,0.00002005354,0.000006055172,0.000004561829,0.0001401558],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8405004,"threshold_uncertainty_score":0.3440332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02810834383475316,"score_gpt":0.2776878064509402,"score_spread":0.249579462616187,"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."}}