{"id":"W1985966190","doi":"10.1109/isspa.2012.6310520","title":"Efficiency evaluation of different wavelets for image compression","year":2012,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Wavelet; Discrete wavelet transform; Artificial intelligence; Image compression; Mathematics; Pattern recognition (psychology); Computer science; Wavelet transform; Data compression; Computer vision; Compression ratio; Set partitioning in hierarchical trees; Image processing; Image (mathematics); Engineering","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":[0.002782913,0.0006353135,0.000666932,0.002473879,0.0002472494,0.0005985373,0.0005408659,0.0007219903,0.0007975792],"category_scores_gemma":[0.008133021,0.0001803816,0.0004865587,0.001984171,0.0003909761,0.001069507,0.0004766333,0.0003021974,0.0003385952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004413188,"about_ca_system_score_gemma":0.0002372574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004894062,"about_ca_topic_score_gemma":0.0003627119,"domain_scores_codex":[0.9984895,0.0004215624,0.0001069889,0.0001099405,0.0007963328,0.00007565899],"domain_scores_gemma":[0.9968182,0.001894436,0.0002114234,0.0003447716,0.0006773865,0.00005385682],"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":[0.002184958,0.0003611908,0.006026682,0.0008296765,0.0002599617,0.0004223118,0.0001969864,0.1515738,0.1809144,0.007280802,0.001446368,0.6485028],"study_design_scores_gemma":[0.00008776502,0.001321872,0.005321959,0.00006836053,0.0001277807,0.0007589207,0.0001049027,0.7975848,0.1900368,0.001670874,0.002861524,0.00005434471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5486741,0.008181767,0.4361809,0.0003798896,0.00009519274,0.000161966,0.0002311434,0.0009871814,0.005107873],"genre_scores_gemma":[0.7796344,0.004767611,0.2129252,0.00005957494,0.0000546841,0.000105356,0.0005025826,0.0002250632,0.001725637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002782913,"threshold_uncertainty_score":0.01471764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06803162977980494,"score_gpt":0.3633427886657863,"score_spread":0.2953111588859814,"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."}}