{"id":"W2793194000","doi":"10.1007/s00034-018-0768-x","title":"Pruned Discrete Tchebichef Transform Approximation for Image Compression","year":2018,"lang":"en","type":"article","venue":"Circuits Systems and Signal Processing","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; University of Akron","keywords":"Discrete cosine transform; Computer science; Transform coding; Data compression; Image compression; Very-large-scale integration; Algorithm; Computational complexity theory; Quantization (signal processing); Coding (social sciences); Context (archaeology); Artificial intelligence; Image processing; Mathematics; Image (mathematics); Embedded system","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003239559,0.0001373501,0.0001682296,0.00007713667,0.0003438394,0.001054602,0.0002201942,0.00004836881,0.000002517769],"category_scores_gemma":[0.000007754184,0.0001144127,0.00003424255,0.0001686537,0.00005924643,0.002116089,0.00002920124,0.00004423293,0.000004618188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002464188,"about_ca_system_score_gemma":0.00005038564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008109237,"about_ca_topic_score_gemma":9.195631e-7,"domain_scores_codex":[0.9988666,0.00002997136,0.0003114219,0.0003352596,0.0002085145,0.0002482025],"domain_scores_gemma":[0.9994255,0.00003667741,0.0001385518,0.0001211547,0.0001920963,0.00008601086],"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":[0.00001475505,0.00003848018,0.00004654506,0.0009230461,0.00001524495,0.000001501268,0.004513571,0.000005684335,0.1341959,0.01178246,0.0004554115,0.8480074],"study_design_scores_gemma":[0.001713422,0.0006178702,0.0003837352,0.0007219519,0.0000265265,0.00006494925,0.0007142067,0.9277052,0.05134721,0.01122007,0.00491504,0.0005698082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00439864,0.0002514435,0.9901633,0.000111723,0.0001322124,0.0006314846,0.000008112451,0.0001129443,0.004190181],"genre_scores_gemma":[0.9950085,0.00000171919,0.00445196,0.00006172516,0.0001856187,0.00007324911,0.00001887315,0.00001248257,0.0001859256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9906098,"threshold_uncertainty_score":0.9999824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03530420625449224,"score_gpt":0.2871546250403907,"score_spread":0.2518504187858984,"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."}}