{"id":"W4402307192","doi":"10.18280/ts.410448","title":"Low-Power Approximate SAD Design for Efficient Integer Motion Estimation in Video Compression","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Integer (computer science); Estimation; Compression (physics); Data compression; Computer science; Motion estimation; Power (physics); Quarter-pixel motion; Artificial intelligence; Computer vision; Mathematics; Engineering; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007788726,0.0002197333,0.000196973,0.0003295448,0.00009610033,0.0002445865,0.0005896115,0.00007515747,0.00006298011],"category_scores_gemma":[0.0000371325,0.0001855568,0.00007413824,0.0003838718,0.00003641932,0.0007504432,0.0002105253,0.000166045,0.00002482785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001597828,"about_ca_system_score_gemma":0.00004271287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002745856,"about_ca_topic_score_gemma":4.341245e-7,"domain_scores_codex":[0.9980962,0.0001167905,0.0004649276,0.0006125525,0.0003840933,0.000325427],"domain_scores_gemma":[0.9991068,0.0002902159,0.00008967747,0.0003712707,0.00006773683,0.00007426804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001625668,0.0007654037,0.00001480093,0.000374739,0.00002813865,0.00003837189,0.001750873,0.4426205,0.09336228,0.05601886,0.01008918,0.3947743],"study_design_scores_gemma":[0.0003647774,0.0001375415,0.00007875409,0.0005102025,0.000004825599,0.000004591559,0.000009572874,0.8988969,0.09233212,0.006365716,0.001101654,0.0001933169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002795452,0.0001280249,0.9944555,0.000334244,0.0003012618,0.001183835,0.00001740161,0.0007267327,0.00005759976],"genre_scores_gemma":[0.6624225,0.000004189281,0.3370552,0.00009806039,0.00002279387,0.0003298458,0.00003082661,0.00001677679,0.0000198758],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.659627,"threshold_uncertainty_score":0.7566786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02313803246065438,"score_gpt":0.2901979003664025,"score_spread":0.2670598679057481,"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."}}