{"id":"W1925805987","doi":"10.1109/icip.1999.819594","title":"Compressed domain motion vector resampling for downscaling of MPEG video","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Motion vector; Block-matching algorithm; Motion compensation; Computer vision; Video compression picture types; Motion estimation; Quarter-pixel motion; Artificial intelligence; Compressed sensing; Uncompressed video; Video tracking; Video processing; 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":[0.0002614268,0.0003666726,0.0002392487,0.0005696117,0.0001948299,0.000223657,0.000295181,0.0003062238,0.002558524],"category_scores_gemma":[0.001134844,0.0001077046,0.0002579095,0.0004883224,0.0001934537,0.0003938396,0.0002361725,0.0003692605,0.0005924335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001806792,"about_ca_system_score_gemma":0.0002609889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001169304,"about_ca_topic_score_gemma":0.002218732,"domain_scores_codex":[0.99984,0.00003305074,0.000006515143,0.00001529728,0.00009423553,0.0000108187],"domain_scores_gemma":[0.9998057,0.00006812661,0.00002018518,0.0000440504,0.00005430574,0.000007570915],"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":[0.0003240996,0.0001023385,0.0007934343,0.000205955,0.00004997139,0.0002790394,0.00009653811,0.02591776,0.2303823,0.01825347,0.00659086,0.7170042],"study_design_scores_gemma":[0.0001301143,0.0005495255,0.005341014,0.00007167021,0.00009250301,0.002029309,0.00009478049,0.6268267,0.3018088,0.009782358,0.0532061,0.00006714006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03559605,0.001688048,0.9559861,0.000337383,0.0001880963,0.0002016751,0.0001456047,0.000898112,0.004958867],"genre_scores_gemma":[0.2641759,0.00192159,0.7275596,0.0002119615,0.0001784161,0.0001616487,0.0006434116,0.0001256996,0.005021953],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002558524,"threshold_uncertainty_score":0.008559108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03237085012529316,"score_gpt":0.2650861103031661,"score_spread":0.232715260177873,"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."}}