{"id":"W2518172581","doi":"10.1109/icip.2016.7532709","title":"Sub-partition reuse for fast optimal motion estimation in HEVC successive elimination algorithms","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Encoder; Motion estimation; Speedup; Reference software; Computer science; Coding (social sciences); Algorithm; Reuse; Partition (number theory); Bit rate; Mathematical optimization; Software; Mathematics; Real-time computing; Parallel computing; 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":[0.000250968,0.0001026286,0.0001047496,0.0002384679,0.00009196289,0.00009444758,0.0005900874,0.00009320533,0.000005568083],"category_scores_gemma":[0.0004627701,0.00007146227,0.00003765647,0.0002664741,0.00003083835,0.001146533,0.0001903522,0.00004864403,0.00002428858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008867827,"about_ca_system_score_gemma":0.00002071954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001095848,"about_ca_topic_score_gemma":0.000007474368,"domain_scores_codex":[0.9990268,0.00003466247,0.0002304574,0.0003302871,0.0001743456,0.000203488],"domain_scores_gemma":[0.9991136,0.0001685456,0.0001098921,0.0004515035,0.0001271939,0.00002927157],"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":[0.00001050888,0.00005740699,0.0001106193,0.000008932802,0.000002659289,0.000001514039,0.0001221285,0.001194476,0.01192916,0.0416328,0.0007231864,0.9442066],"study_design_scores_gemma":[0.0006064111,0.0001315303,0.002544774,0.00009405617,0.000002450302,0.000002969444,0.00004063745,0.6923081,0.2845302,0.01949899,0.00009180291,0.0001479975],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0548376,0.00001658809,0.9372938,0.006902867,0.0001730531,0.0002184739,0.000002570568,0.0004848465,0.00007016537],"genre_scores_gemma":[0.856843,0.00001614985,0.1427605,0.00003625967,0.0000166267,0.0001330578,0.00000468907,0.000005000444,0.0001847643],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9440586,"threshold_uncertainty_score":0.2914147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02025754801145533,"score_gpt":0.2719029640056463,"score_spread":0.251645415994191,"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."}}