{"id":"W2038364458","doi":"10.1109/sips.2006.352568","title":"Motion Compensated Frame Rate Conversion Using a Specialized Instruction Set Processor","year":2006,"lang":"en","type":"article","venue":"SiPS ... design and implementation - IEEE Workshop on Signal Processing Systems","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Acceleration; Frame rate; Application-specific instruction-set processor; NTSC; Set (abstract data type); Instruction set; Frame (networking); Motion estimation; Bandwidth (computing); Block (permutation group theory); Algorithm; Artificial intelligence; Real-time computing; Parallel computing; High-definition television; Mathematics","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008205423,0.0003345408,0.0003625517,0.0004206733,0.0008000763,0.00104638,0.0003916299,0.0001867735,0.00001572619],"category_scores_gemma":[0.00001725145,0.0003037754,0.0000539075,0.0008683053,0.00009136901,0.001063583,0.00006945983,0.0002547752,0.0000115686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001649618,"about_ca_system_score_gemma":0.0001273733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001068035,"about_ca_topic_score_gemma":0.000002873306,"domain_scores_codex":[0.9972496,0.0004285616,0.0006942371,0.0006979287,0.0005085167,0.000421158],"domain_scores_gemma":[0.9986011,0.0001621431,0.0006234104,0.0002619028,0.0002686379,0.00008285602],"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":[0.0008875761,0.0004557333,0.001711907,0.001370406,0.0001500321,0.00006327417,0.003447619,0.128263,0.348694,0.01342842,0.01136072,0.4901673],"study_design_scores_gemma":[0.003140504,0.0002453626,0.0003924592,0.0008827927,0.00004500286,0.00008207859,0.002520858,0.9174756,0.07029109,0.003332597,0.0008938047,0.0006978724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1592651,0.0002425,0.8384603,0.0001836825,0.0004768415,0.0007180363,0.000004288072,0.0005935526,0.00005573189],"genre_scores_gemma":[0.9901393,0.00002082412,0.009334317,0.0001042741,0.0001839105,0.00006590679,0.00002505511,0.00002522568,0.000101152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8308743,"threshold_uncertainty_score":0.9999906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06964393935627262,"score_gpt":0.3195014404014105,"score_spread":0.2498575010451379,"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."}}