{"id":"W2149862911","doi":"10.1109/tcsvt.2009.2020322","title":"Adaptive Variable Block-Size Early Motion Estimation Termination Algorithm for H.264/AVC Video Coding Standard","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Motion estimation; Quarter-pixel motion; Encoder; Algorithm; Block size; Computer science; Coding (social sciences); Block (permutation group theory); Motion compensation; Context-adaptive variable-length coding; Computer vision; Data compression; Mathematics; Context-adaptive binary arithmetic coding; Statistics; Key (lock)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005227706,0.0002984158,0.000462008,0.0007017257,0.0007545213,0.0002692309,0.0005593023,0.0004379634,0.000001179764],"category_scores_gemma":[0.00008845107,0.0002879424,0.0001110794,0.0006967041,0.00009169742,0.0005987486,0.0000067035,0.0002881275,0.000003017837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001699488,"about_ca_system_score_gemma":0.0000676081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002270572,"about_ca_topic_score_gemma":0.000002537515,"domain_scores_codex":[0.9979306,0.00005081715,0.0005351868,0.0007413411,0.0002775762,0.0004645072],"domain_scores_gemma":[0.9982689,0.0004157198,0.0002813777,0.0005842791,0.0003762382,0.00007351062],"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":[0.00002080236,0.00008549708,0.000002272908,0.00004928696,0.00003963263,0.000002229427,0.0001144601,0.00249545,0.004011765,0.03890042,0.000185547,0.9540926],"study_design_scores_gemma":[0.001930218,0.002832447,0.00004353582,0.0003813664,0.00007357693,0.0001300923,0.0002514432,0.886425,0.05386594,0.05183385,0.001701439,0.000531051],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002603501,0.0002267571,0.9921716,0.0008709838,0.0009058315,0.001535446,0.00009222046,0.001543101,0.0000505463],"genre_scores_gemma":[0.9502454,0.00004497157,0.04861992,0.00006413071,0.00003647925,0.0007507537,0.000002147804,0.00002045971,0.0002156872],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9535616,"threshold_uncertainty_score":0.9999573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056278375780106,"score_gpt":0.2542664049140915,"score_spread":0.2337036211562905,"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."}}