{"id":"W2165479634","doi":"10.1109/icip.1997.632203","title":"Overlapped variable size block motion compensation","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Motion compensation; Computer vision; Residual; Quarter-pixel motion; Block (permutation group theory); Coding (social sciences); Artificial intelligence; Motion estimation; Block size; Compensation (psychology); Blocking (statistics); Frame (networking); Algorithmic efficiency; Intra-frame; Inter frame; Reference frame; Algorithm; Pixel; Mathematics","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":[0.00009368805,0.00008072407,0.00008832116,0.00005355424,0.0001242201,0.000124888,0.0005494161,0.00006406964,0.0002539839],"category_scores_gemma":[0.00009631628,0.0000675789,0.0000269,0.0003271964,0.00001724643,0.0003059251,0.0001937678,0.00008672501,0.0002600304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002008223,"about_ca_system_score_gemma":0.00000434946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001575192,"about_ca_topic_score_gemma":6.608052e-7,"domain_scores_codex":[0.9992599,0.00002627191,0.0001328506,0.000237972,0.0001787561,0.000164228],"domain_scores_gemma":[0.9992964,0.00009682246,0.00004913506,0.0004809661,0.00004541656,0.00003125959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002124107,0.0002069054,0.0006603497,0.00001328932,0.000015605,0.000005993291,0.0002196969,0.0009557595,0.01505668,0.7677334,0.05771698,0.1574132],"study_design_scores_gemma":[0.0005276549,0.00009935805,0.002186806,0.00003121178,0.000004638069,0.00002749088,0.00004166038,0.9056867,0.02456213,0.04490228,0.0216085,0.0003216199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01272314,0.00008719225,0.9116936,0.002976999,0.0003653576,0.000107639,4.03432e-7,0.00235022,0.06969548],"genre_scores_gemma":[0.9281008,0.00001506388,0.06649837,0.0002906968,0.00001866162,0.000007286938,2.224069e-7,0.000003269925,0.005065603],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9153777,"threshold_uncertainty_score":0.3342252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02438186150893655,"score_gpt":0.2138311343959195,"score_spread":0.189449272886983,"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."}}