{"id":"W2105296277","doi":"10.1109/isiea.2009.5356471","title":"Speeding up motion estimation in modern video encoders using approximate metrics and SIMD processors","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"SIMD; Computer science; Motion estimation; Encoder; Motion (physics); Estimation; Computer graphics (images); Parallel computing; Computer vision; Artificial intelligence; Engineering","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":[0.000657895,0.0005698131,0.000371522,0.001008717,0.0002631262,0.0006756181,0.0004883343,0.000345102,0.001774107],"category_scores_gemma":[0.00269363,0.0002808866,0.0001738414,0.001155899,0.0004324754,0.001706062,0.0007330878,0.0005594904,0.0005487509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004473758,"about_ca_system_score_gemma":0.000469484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000823048,"about_ca_topic_score_gemma":0.001554767,"domain_scores_codex":[0.999527,0.0001041355,0.00003329213,0.0000491637,0.0002638354,0.00002264736],"domain_scores_gemma":[0.9994426,0.0002497933,0.00005663304,0.0001045619,0.0001289603,0.00001753552],"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":[0.0004697868,0.00005624963,0.002566604,0.0002532746,0.0000433016,0.00009576491,0.0002122351,0.05976329,0.08911194,0.04014817,0.002586359,0.804693],"study_design_scores_gemma":[0.00004639306,0.0003723735,0.001272476,0.00004877453,0.0000243161,0.0003166643,0.00006347254,0.8809569,0.08395398,0.01973324,0.01317583,0.00003555702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04848627,0.001642335,0.9459379,0.000161935,0.00006604017,0.00005764419,0.00006505397,0.001224375,0.002358559],"genre_scores_gemma":[0.2740007,0.0008712643,0.7231399,0.00009330018,0.00005027209,0.00007220723,0.0001590295,0.00007729304,0.001535994],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001774107,"threshold_uncertainty_score":0.005934954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04586642562190932,"score_gpt":0.2883218803873429,"score_spread":0.2424554547654335,"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."}}