{"id":"W2141086585","doi":"10.1109/iscas.2006.1693463","title":"Scalable High-Throughput Architecture for H.264/AVC Variable Block Size Motion Estimation","year":2006,"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":"Queen's University","funders":"","keywords":"Block size; Computer science; Motion estimation; Throughput; Motion vector; Very-large-scale integration; Block (permutation group theory); Scalability; Quarter-pixel motion; Pixel; Real-time computing; Computer hardware; Parallel computing; Computer engineering; Computer architecture; Algorithm; Key (lock); Embedded system; Artificial intelligence; Mathematics; Image (mathematics); Telecommunications","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":[0.0001922285,0.000157605,0.0001687312,0.00009021369,0.0002561561,0.0002254544,0.0006895573,0.0001348145,0.00002106691],"category_scores_gemma":[0.0001526873,0.000127307,0.00005551318,0.0004070766,0.00003481227,0.0003282571,0.0002182537,0.0001202411,0.00002927447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003971133,"about_ca_system_score_gemma":0.00003502808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002407623,"about_ca_topic_score_gemma":0.000009491064,"domain_scores_codex":[0.9987643,0.00002635592,0.0002381979,0.0004367878,0.000214274,0.0003200878],"domain_scores_gemma":[0.9989219,0.000247991,0.00009490655,0.0006059107,0.00009726798,0.00003204148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001200133,0.0001671049,0.00008674573,0.00005845301,0.00001363536,0.000001812891,0.00004087115,0.1220092,0.008746339,0.6642997,0.0525588,0.1520053],"study_design_scores_gemma":[0.0005447169,0.0001124895,0.0004106807,0.00004238629,0.000008609905,0.00001399825,0.000008233738,0.3911401,0.06833587,0.5263427,0.01278269,0.0002574956],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004857027,0.00005351843,0.9873586,0.003167113,0.0003060004,0.0002911123,0.000003335455,0.001624422,0.002338926],"genre_scores_gemma":[0.4445582,0.000001670846,0.551757,0.0001374047,0.00005138513,0.00006713154,0.000004380496,0.000007514848,0.00341526],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4397012,"threshold_uncertainty_score":0.5191427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008969118659551063,"score_gpt":0.2216685210226746,"score_spread":0.2126994023631235,"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."}}