{"id":"W2088783711","doi":"10.1049/el:20030318","title":"Error-free computation of Daubechies wavelets for image compression applications","year":2003,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Wavelet; Transformation (genetics); Computation; Image compression; Algorithm; Image (mathematics); Encoding (memory); Compression (physics); Matrix (chemical analysis); Data compression; Daubechies wavelet; Transformation matrix; Algebraic number; Mathematics; Computer science; Wavelet transform; Image processing; Artificial intelligence; Discrete wavelet transform; Mathematical analysis","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":[0.0006519597,0.0005844463,0.0004535249,0.0005348606,0.000326484,0.0006795115,0.000601254,0.0005374336,0.001154301],"category_scores_gemma":[0.002588416,0.0002286712,0.000225035,0.0007233046,0.0003201755,0.0008943464,0.0007391298,0.0008907019,0.0005784938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002457837,"about_ca_system_score_gemma":0.0005073248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005429946,"about_ca_topic_score_gemma":0.0007723664,"domain_scores_codex":[0.999651,0.00007737213,0.00002576017,0.00002257988,0.0001992507,0.00002408593],"domain_scores_gemma":[0.9994947,0.0001937436,0.00003959002,0.000112375,0.0001314717,0.00002817156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000392814,0.00008046308,0.0007757291,0.0002578604,0.00004889743,0.0003624331,0.0002647548,0.11861,0.1518118,0.1937112,0.004278386,0.5294057],"study_design_scores_gemma":[0.00003395221,0.00006827895,0.0001954846,0.0000217033,0.0000105741,0.0001831374,0.00002150034,0.9279024,0.04270359,0.02286983,0.005970702,0.0000189431],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01271118,0.0002941511,0.9857078,0.00009971176,0.00005500182,0.00001865737,0.00002478724,0.0001999088,0.0008887415],"genre_scores_gemma":[0.1578215,0.0006789885,0.8384865,0.00005508302,0.00006260395,0.00008290543,0.000145578,0.0001031094,0.0025638],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001154301,"threshold_uncertainty_score":0.003861547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01528650149662693,"score_gpt":0.2871153290208038,"score_spread":0.2718288275241769,"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."}}