{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001273806,0.0001152963,0.0001285252,0.0003036332,0.0004115623,0.0004727551,0.0005196613,0.00005487573,0.000007232656],"category_scores_gemma":[0.0000339202,0.00009300783,0.00002006919,0.0004400183,0.000321331,0.01390575,0.0003010003,0.0001154724,0.000006086729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001411566,"about_ca_system_score_gemma":0.00009577669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000310992,"about_ca_topic_score_gemma":8.447556e-8,"domain_scores_codex":[0.9986184,0.00005252235,0.0003280838,0.0001925337,0.000576698,0.0002318057],"domain_scores_gemma":[0.9992675,0.00004415935,0.00009607096,0.0001967093,0.0001925585,0.0002030575],"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":[0.000004109816,0.00001159382,0.00001863586,0.000017064,0.00000165674,0.000002385884,0.005949934,0.00004203003,0.0004530438,0.005863269,0.00002657343,0.9876097],"study_design_scores_gemma":[0.0005309691,0.00004661464,0.005443766,0.00004111949,0.000002148903,0.0001387501,0.00003282499,0.9863042,0.006459347,0.0006328024,0.0002331039,0.0001344012],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01652364,0.00003691306,0.9805844,0.00008652086,0.0003145021,0.000114977,0.000001288423,0.00007460303,0.002263141],"genre_scores_gemma":[0.3461488,0.00004600032,0.6530523,0.0007156932,0.00002951332,0.000001775398,0.000001404703,0.000001883839,0.000002585512],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9874753,"threshold_uncertainty_score":0.9998863,"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."}}