{"id":"W4205700951","doi":"10.1109/tcsvt.2021.3134054","title":"A Class of Low-Complexity DCT-Like Transforms for Image and Video Coding","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"BP (Canada)","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; JPEG; Algorithm; Transform coding; Computational complexity theory; Data compression; Decorrelation; Trellis quantization; Computer science; Image compression; Coding (social sciences); Mathematics; Theoretical computer science; Image processing; Computer vision; Artificial intelligence; Image (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":[],"consensus_categories":[],"category_scores_codex":[0.0002684855,0.0001635388,0.0003470551,0.0002914991,0.0002002282,0.0001565341,0.0002110354,0.0001233655,0.000002030548],"category_scores_gemma":[0.00001167165,0.0001566967,0.00009597081,0.0003468031,0.0001413077,0.0005044298,0.000003514634,0.00009663968,9.770516e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003507367,"about_ca_system_score_gemma":0.00006436921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001868993,"about_ca_topic_score_gemma":0.00004140354,"domain_scores_codex":[0.9986801,0.00002929208,0.0004282383,0.0004490177,0.0001271741,0.0002862002],"domain_scores_gemma":[0.9991103,0.0002100448,0.0001045186,0.0003077786,0.0002012104,0.00006616714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008032005,0.0003924167,0.00003391149,0.003326481,0.0002522685,0.00001591497,0.000951372,0.00007286101,0.3808574,0.3365745,0.0005375472,0.276905],"study_design_scores_gemma":[0.007932534,0.002521295,0.0001243369,0.0008233303,0.0001614163,0.000803327,0.001821815,0.07198658,0.8402956,0.06047448,0.01199546,0.001059858],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01624082,0.0002021175,0.981188,0.0004783504,0.0004992614,0.0008671864,0.0002276441,0.0001434271,0.0001532003],"genre_scores_gemma":[0.9965289,0.0000586664,0.002861955,0.00007792857,0.00001202101,0.0002942301,0.00000700039,0.00001528835,0.0001439628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9802881,"threshold_uncertainty_score":0.6389908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04549243187181989,"score_gpt":0.2799412699927863,"score_spread":0.2344488381209664,"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."}}