{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004351377,0.0003960015,0.0005361578,0.00036177,0.0002125945,0.0004754666,0.0006630252,0.0005318547,0.001524953],"category_scores_gemma":[0.001093396,0.000222792,0.0003152923,0.0004430134,0.0002272738,0.000400929,0.0004112292,0.000638011,0.0004118251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002538452,"about_ca_system_score_gemma":0.001074016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005539513,"about_ca_topic_score_gemma":0.009397755,"domain_scores_codex":[0.9997029,0.00006950966,0.0000163658,0.00003583815,0.0001486076,0.00002686907],"domain_scores_gemma":[0.9997148,0.000102957,0.00002077934,0.00004184212,0.0001069365,0.00001278206],"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":[0.0002919284,0.0001125286,0.0007387813,0.0001481137,0.00009527314,0.0001478741,0.0001251853,0.380163,0.0680198,0.01862213,0.004586123,0.5269493],"study_design_scores_gemma":[0.000006989379,0.00003295777,0.0001280059,0.000004812304,0.000006420969,0.00004285366,0.000005007217,0.9933538,0.004602888,0.0007265077,0.001084652,0.00000514351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009252828,0.0002653141,0.9890172,0.00005786755,0.00003679411,0.00002235981,0.00002225219,0.0001643377,0.001160866],"genre_scores_gemma":[0.2632774,0.0004421469,0.7304541,0.0001067662,0.0000574153,0.0000848705,0.0002082164,0.00007111852,0.005297918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005539513,"threshold_uncertainty_score":0.01101452,"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."}}