{"id":"W2148838455","doi":"10.1109/iembs.2005.1617305","title":"Comparison of JPEG 2000 and Other Lossless Compression Schemes for Digital Mammograms","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Lossless JPEG; JPEG; Huffman coding; Lossless compression; Lossy compression; Computer science; Computer vision; Data compression; Artificial intelligence; JPEG 2000; Compression artifact; Image compression; Image processing; Image (mathematics)","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":[0.001157384,0.0004294234,0.0002649823,0.001600064,0.0001945028,0.0004957956,0.0005116428,0.0003824399,0.00141929],"category_scores_gemma":[0.003928063,0.0001133433,0.0002384075,0.001115159,0.0002391242,0.0009700218,0.0002234541,0.0002268372,0.0003537074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000431409,"about_ca_system_score_gemma":0.0003433403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001571171,"about_ca_topic_score_gemma":0.001862222,"domain_scores_codex":[0.9991789,0.0001332332,0.00005109164,0.0000498416,0.0005382334,0.00004859384],"domain_scores_gemma":[0.9987527,0.0004717269,0.0001061319,0.0001466817,0.0004571133,0.00006566178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004780814,0.0005284921,0.005782552,0.001202992,0.0003032896,0.0003726927,0.0002178424,0.03643477,0.1893283,0.006915194,0.005306201,0.7488269],"study_design_scores_gemma":[0.000949124,0.008095874,0.04415774,0.0002761356,0.0006626262,0.004052563,0.0003151349,0.4221788,0.4706483,0.003387425,0.045052,0.0002242608],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7624219,0.01876024,0.2016508,0.000554258,0.0004865463,0.0006063859,0.0006077423,0.001670733,0.01324137],"genre_scores_gemma":[0.7801842,0.008359635,0.2004164,0.0003203417,0.0001898008,0.0001412976,0.001445148,0.0001890376,0.00875412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001600064,"threshold_uncertainty_score":0.00612092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03636457976219418,"score_gpt":0.3608282614921168,"score_spread":0.3244636817299227,"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."}}