{"id":"W2047321635","doi":"10.5539/cis.v4n6p2","title":"Medical Images Compression Using Modified SPIHT Algorithm and Multiwavelets Transformation","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Set partitioning in hierarchical trees; Computer science; Wavelet; Wavelet transform; Image compression; Algorithm; Data compression; Artificial intelligence; Shearlet; Discrete wavelet transform; Pattern recognition (psychology); Computer vision; Image processing; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003062583,0.0003602912,0.00038448,0.0007784605,0.0001520774,0.0004401391,0.0004261585,0.0005909508,0.001487917],"category_scores_gemma":[0.0008671981,0.000147861,0.0005231682,0.0008660705,0.0002634553,0.0006792014,0.0003066472,0.0004505419,0.0005677955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002345326,"about_ca_system_score_gemma":0.0002917284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004075403,"about_ca_topic_score_gemma":0.0004263355,"domain_scores_codex":[0.9996915,0.00003999072,0.0000181505,0.00002958959,0.0002084022,0.00001241551],"domain_scores_gemma":[0.9997948,0.00007580069,0.00002833816,0.00002779871,0.00006590535,0.000007400696],"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":[0.0002712035,0.0001169835,0.0005214729,0.0003517267,0.00007322262,0.000455919,0.0001779867,0.132469,0.1697325,0.02459092,0.002828754,0.6684103],"study_design_scores_gemma":[0.0000313149,0.0002891856,0.000809464,0.00003109706,0.00003338085,0.000984825,0.00003193844,0.9269359,0.05657038,0.007646637,0.006609678,0.00002619908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01840718,0.0007130451,0.9781953,0.0001471122,0.00008401745,0.00005736961,0.00005793315,0.0003520987,0.001985896],"genre_scores_gemma":[0.1590302,0.0016334,0.8347491,0.0000911951,0.0001141844,0.0001177248,0.0002384187,0.00007300054,0.003952811],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001487917,"threshold_uncertainty_score":0.004977584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04138393418948882,"score_gpt":0.2954632873192671,"score_spread":0.2540793531297783,"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."}}