{"id":"W2991413467","doi":"10.1016/j.image.2019.115719","title":"Multi-level rate-constrained successive elimination algorithm tailored to suboptimal motion estimation in HEVC","year":2019,"lang":"en","type":"article","venue":"Signal Processing Image Communication","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Motion estimation; Encoder; Motion vector; Algorithm; Computer science; Leverage (statistics); Computation; Search algorithm; Reference software; Computational complexity theory; Coding (social sciences); Mathematical optimization; Mathematics; Artificial intelligence; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008298286,0.0002201186,0.0002337312,0.0004931215,0.0002639493,0.0005099843,0.001591368,0.000141222,0.00001475435],"category_scores_gemma":[0.0002479448,0.0002231081,0.00004606416,0.001017647,0.0000872021,0.002185385,0.0005240607,0.0003470678,0.000108894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001661159,"about_ca_system_score_gemma":0.0001170741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000447521,"about_ca_topic_score_gemma":0.000009490275,"domain_scores_codex":[0.9981654,0.0002566419,0.0004720667,0.0004956609,0.0003006181,0.0003096116],"domain_scores_gemma":[0.9981501,0.0001846643,0.000322106,0.0008636981,0.0004149435,0.0000644915],"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":[0.00001552316,0.000145877,0.000153899,0.00004017268,0.000003967883,0.000002111358,0.0009858964,0.004782467,0.0399554,0.0005600052,0.00003388087,0.9533208],"study_design_scores_gemma":[0.0006872981,0.00008166474,0.008617711,0.0003239906,0.00000458868,0.000004976505,0.0003177425,0.9582996,0.02975488,0.001624617,0.00001687099,0.0002660591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02086399,0.0001851454,0.975154,0.002575678,0.00005673325,0.0004746092,0.000002995858,0.0005097055,0.0001771764],"genre_scores_gemma":[0.5823362,0.000008347511,0.4173907,0.00007766215,0.0000046577,0.00006254548,0.00002923494,0.00001007844,0.00008052569],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9535171,"threshold_uncertainty_score":0.9098085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03238993725644982,"score_gpt":0.2939228739616086,"score_spread":0.2615329367051588,"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."}}