{"id":"W3094861295","doi":"10.1016/j.bspc.2020.102306","title":"An optimized JPEG-XT-based algorithm for the lossy and lossless compression of 16-bit depth medical image","year":2020,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Office of Research and Economic Development, Louisiana State University; Office of Research and Economic Development, Mississippi State University; Louisiana State University; Royal College of Surgeons of England; Health Sciences Center New Orleans, Louisiana State University; Louisiana Board of Regents","keywords":"JPEG; Lossless JPEG; Lossless compression; Lossy compression; JPEG 2000; Discrete cosine transform; Image compression; Mathematics; Algorithm; Data compression; Computer science; Artificial intelligence; Computer vision; 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":[0.0003023821,0.0005367885,0.0003207508,0.0007073817,0.0002249612,0.0005070997,0.0006587838,0.0004880585,0.003822579],"category_scores_gemma":[0.0007796074,0.0001728532,0.0002687695,0.0007238632,0.0001588684,0.0005399179,0.0003875823,0.0005271934,0.001523262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003499403,"about_ca_system_score_gemma":0.0009984034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002248265,"about_ca_topic_score_gemma":0.005747091,"domain_scores_codex":[0.9997572,0.00002340234,0.00001795454,0.00002367387,0.000162793,0.00001486406],"domain_scores_gemma":[0.9997808,0.00003500892,0.00001893096,0.00002806874,0.0001217692,0.00001534503],"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":[0.0004543222,0.0001072551,0.0005943921,0.0002475703,0.000064592,0.0001599691,0.0000667337,0.0158989,0.2115381,0.006436274,0.01037215,0.7540597],"study_design_scores_gemma":[0.0001730712,0.0004684107,0.004660689,0.00007375979,0.0001194504,0.00208592,0.00004936866,0.6864628,0.2704669,0.002572028,0.03277941,0.00008819753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01862747,0.0009885541,0.975937,0.0001653938,0.0001790702,0.0002046298,0.0002815563,0.00132693,0.002289372],"genre_scores_gemma":[0.06542072,0.0006863998,0.9266001,0.0001475438,0.00006701704,0.0001420776,0.0007234173,0.0001781978,0.006034609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003822579,"threshold_uncertainty_score":0.01278776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01461918221432178,"score_gpt":0.2877301801156462,"score_spread":0.2731109979013244,"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."}}