{"id":"W2731588245","doi":"10.1016/j.image.2017.06.014","title":"DCT approximations based on Chen’s factorization","year":2017,"lang":"en","type":"article","venue":"Signal Processing Image Communication","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Discrete cosine transform; Chen; JPEG; Factorization; Computer science; Algorithm; Coding (social sciences); Approximations of π; Transform coding; Computational complexity theory; Data compression; Theoretical computer science; Artificial intelligence; Mathematics; Image (mathematics); Applied 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003148993,0.0007149316,0.0004284984,0.0007580839,0.0003371868,0.000647917,0.0003401983,0.0006845105,0.006886418],"category_scores_gemma":[0.001610316,0.0002414735,0.0005883474,0.0009140004,0.0003579771,0.0007349717,0.0003422482,0.0009576607,0.002163599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003817154,"about_ca_system_score_gemma":0.0005957985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003143976,"about_ca_topic_score_gemma":0.005479313,"domain_scores_codex":[0.999689,0.00005727026,0.00001737777,0.00004729019,0.0001617519,0.00002741303],"domain_scores_gemma":[0.9995161,0.00015939,0.00003122225,0.00009637806,0.000175823,0.00002107343],"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":[0.0004833427,0.00007817168,0.0007411781,0.0003180227,0.00007895869,0.0003675146,0.0001867227,0.09810859,0.149639,0.09716121,0.01041524,0.6424222],"study_design_scores_gemma":[0.00004210403,0.0001390881,0.0006518119,0.00004359764,0.00004427323,0.0005903175,0.00004785397,0.9108725,0.04935369,0.01002804,0.02814408,0.00004259635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006679439,0.0004424213,0.9886746,0.0001265996,0.0001783738,0.00003232968,0.00006620311,0.00029215,0.003507891],"genre_scores_gemma":[0.175531,0.001458462,0.807578,0.0001978118,0.0002641101,0.00008759322,0.0004709754,0.0001724822,0.01423941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006886418,"threshold_uncertainty_score":0.02303737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05255052972174097,"score_gpt":0.3222346192773068,"score_spread":0.2696840895555659,"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."}}