{"id":"W4233717654","doi":"10.32920/ryerson.14656704.v1","title":"Analysis and architecture design of scalable fractional motion estimation for H.264 encoding","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Strong","keywords":"Computer science; Encoder; Field-programmable gate array; Speedup; Scalability; Motion estimation; Block (permutation group theory); Encoding (memory); Parallel computing; Scaling; Computer hardware; Computer engineering; Algorithm; Artificial intelligence","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":[0.0001318212,0.0003320255,0.0001791149,0.0003165271,0.0002240031,0.0003772108,0.0003844168,0.0002069506,0.002177414],"category_scores_gemma":[0.000322737,0.0001442282,0.0001980095,0.0002943565,0.0001338397,0.0003526529,0.000121105,0.0001759723,0.0003117601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005688586,"about_ca_system_score_gemma":0.0007473162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004902223,"about_ca_topic_score_gemma":0.006665454,"domain_scores_codex":[0.9998704,0.00002010387,0.000006298646,0.0000219854,0.00005701861,0.00002415192],"domain_scores_gemma":[0.9998797,0.00002996762,0.00001963914,0.00001368445,0.00005129054,0.000005711947],"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":[0.0002991407,0.00007735572,0.00273883,0.0003710126,0.00007403274,0.0004002388,0.0001599251,0.4350244,0.2668279,0.01413671,0.005130842,0.2747597],"study_design_scores_gemma":[0.00002718611,0.0002643699,0.001555508,0.00002490972,0.00004129705,0.0001659928,0.00004296344,0.9535531,0.03656719,0.001433679,0.006311108,0.00001270747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2414322,0.00246992,0.7338568,0.0003551385,0.00009163136,0.0001441962,0.0001755945,0.00151463,0.01995977],"genre_scores_gemma":[0.8435752,0.0005127911,0.151792,0.00007799338,0.00002396508,0.0000675964,0.0001806141,0.00006469197,0.00370518],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004902223,"threshold_uncertainty_score":0.009747386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03763376276098712,"score_gpt":0.2805002874547073,"score_spread":0.2428665246937202,"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."}}