{"id":"W2171578425","doi":"10.1109/icip.2009.5414233","title":"Efficient motion vector re-estimation for MPEG-2 TO H.264/AVC transcoding with arbitrary down-sizing ratios","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Motion vector; Quarter-pixel motion; Transcoding; Motion estimation; Computer science; Computational complexity theory; Partition (number theory); Sizing; Algorithm; Computer vision; Reduction (mathematics); Artificial intelligence; Mathematics; Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004028966,0.0008632635,0.0005025017,0.0007425503,0.0002489091,0.0003801722,0.000693387,0.0004070369,0.001627647],"category_scores_gemma":[0.001570676,0.000367223,0.0003931712,0.0004388784,0.000210106,0.0007990196,0.0004196435,0.000594417,0.0008471035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002716144,"about_ca_system_score_gemma":0.0004694231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002620678,"about_ca_topic_score_gemma":0.004621761,"domain_scores_codex":[0.9996804,0.00006049447,0.00002598274,0.00005303482,0.0001520584,0.0000280693],"domain_scores_gemma":[0.9996743,0.00009757514,0.00004233448,0.00007080089,0.0001031481,0.00001175739],"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":[0.0003904154,0.00006927007,0.0007441853,0.00009695879,0.00002892507,0.000114608,0.00008694232,0.02877104,0.3108633,0.003029561,0.002150631,0.6536543],"study_design_scores_gemma":[0.00005487411,0.0001652401,0.002493418,0.00002303714,0.00005154489,0.0005032207,0.00004401373,0.7636798,0.225902,0.001891285,0.005142631,0.00004896171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0127604,0.0002848189,0.9856381,0.00003955218,0.00001972673,0.00004498988,0.00003590814,0.0007010785,0.0004754034],"genre_scores_gemma":[0.1540009,0.0004480234,0.8424416,0.00003996808,0.00003335842,0.00008669978,0.0004364379,0.0001419361,0.002371004],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002620678,"threshold_uncertainty_score":0.005445063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02260109831504821,"score_gpt":0.2582289381806515,"score_spread":0.2356278398656033,"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."}}