{"id":"W2157391823","doi":"10.1109/icassp.2008.4517732","title":"Motion vector prediction for improving one bit transform based motion estimation","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Quarter-pixel motion; Motion estimation; Motion compensation; Block-matching algorithm; Computer science; Motion vector; Computer vision; Artificial intelligence; Motion field; Block (permutation group theory); Pixel; Algorithm; Mathematics; Video processing; Image (mathematics); Video tracking","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":[0.0002142895,0.0004207527,0.0002826408,0.0004299855,0.0001007343,0.0002258186,0.0003346076,0.0002809062,0.0009604522],"category_scores_gemma":[0.001123311,0.0001719284,0.0001709935,0.0004422004,0.0001279744,0.0005533965,0.0002296124,0.0003238771,0.000317017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001842439,"about_ca_system_score_gemma":0.0002655716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002263524,"about_ca_topic_score_gemma":0.002940993,"domain_scores_codex":[0.9998435,0.0000244614,0.000008573242,0.00002420932,0.00008802238,0.00001121733],"domain_scores_gemma":[0.9997494,0.0001016421,0.00003745771,0.00002511887,0.00007852766,0.000007865916],"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":[0.0002723834,0.00007367512,0.001880383,0.0001045336,0.00002584733,0.00009823185,0.00007351839,0.1049625,0.2478078,0.005135131,0.001337311,0.6382287],"study_design_scores_gemma":[0.00001519849,0.00009221703,0.001200714,0.00001304899,0.00001378072,0.000146803,0.000007901718,0.9440346,0.05208186,0.0006307437,0.001750417,0.00001255479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0763441,0.0007375863,0.921124,0.00008805181,0.00005410898,0.00002636576,0.00005130983,0.0005631391,0.001011318],"genre_scores_gemma":[0.5878766,0.0009503371,0.4076643,0.00007830757,0.00005077971,0.00004905268,0.0002763101,0.00007456194,0.002979771],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002263524,"threshold_uncertainty_score":0.004500747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05248706696685847,"score_gpt":0.2642740751072517,"score_spread":0.2117870081403932,"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."}}