{"id":"W2101173850","doi":"10.1109/icassp.2006.1660543","title":"Lossless Compression of 4D Medical Images using H.264/AVC","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Lossless compression; Computer science; Data compression; Lossy compression; Image compression; Computer vision; Compression (physics); Artificial intelligence; Context-adaptive binary arithmetic coding; Texture compression; Coding (social sciences); Image (mathematics); Image processing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001966504,0.0001149199,0.0001995039,0.0001284454,0.0001299384,0.00008186814,0.001295039,0.0001237484,0.00006543178],"category_scores_gemma":[0.00005290459,0.00008321134,0.00006184231,0.0002951259,0.0001769134,0.0002218606,0.0007774861,0.0001447424,0.000007611853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001401391,"about_ca_system_score_gemma":0.000069937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002749021,"about_ca_topic_score_gemma":0.000004836581,"domain_scores_codex":[0.9986212,0.00004669221,0.0002960741,0.0002763484,0.0005435434,0.00021617],"domain_scores_gemma":[0.9991072,0.00009890962,0.0001114783,0.0005548265,0.00007730011,0.0000502716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001855297,0.0006570742,0.01091871,0.0001243779,0.00002896179,0.0001273281,0.0001096747,0.001710454,0.135917,0.5542338,0.03442657,0.2617275],"study_design_scores_gemma":[0.0005385673,0.00005852672,0.002409373,0.0003124313,0.000005626695,0.00005243587,0.00004528141,0.2823553,0.6762565,0.03608179,0.001588991,0.0002951429],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1350641,0.0003084073,0.8591452,0.000802138,0.000192813,0.00005574888,8.406437e-7,0.0005710591,0.003859659],"genre_scores_gemma":[0.9201318,0.00001104422,0.07952248,0.00006560387,0.00003390784,0.000002668907,5.594925e-7,0.000005025362,0.0002268821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7850677,"threshold_uncertainty_score":0.339326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01775182055769468,"score_gpt":0.2693075281816029,"score_spread":0.2515557076239082,"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."}}