{"id":"W1514356334","doi":"10.1109/iscas.1998.698766","title":"A multi-transform approach to reversible embedded image compression","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image compression; Wavelet transform; Top-hat transform; Computer science; Transform coding; Artificial intelligence; Data compression; Compression (physics); Texture compression; S transform; Image (mathematics); Algorithm; Computer vision; Discrete cosine transform; Image processing; Discrete wavelet transform; Wavelet; Digital image processing; Materials science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000170777,0.0004297401,0.0003346185,0.0008960492,0.0002566646,0.0004965689,0.0006219065,0.0005085662,0.003187473],"category_scores_gemma":[0.0004262418,0.0001847312,0.0004959674,0.0007056207,0.0006667401,0.0007612715,0.0006277469,0.0008791638,0.0009596398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002407305,"about_ca_system_score_gemma":0.0002980088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002652323,"about_ca_topic_score_gemma":0.0005030993,"domain_scores_codex":[0.999799,0.00002114477,0.00000997091,0.00003225699,0.0001201666,0.00001750843],"domain_scores_gemma":[0.9998796,0.00003081871,0.00001310997,0.00003516262,0.00003487182,0.000006416446],"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":[0.00007795596,0.00007738346,0.0001990561,0.0002742235,0.00005064874,0.0006259026,0.00009220775,0.05088639,0.1633769,0.2582171,0.003860381,0.5222618],"study_design_scores_gemma":[0.00002828199,0.0003564842,0.0006628779,0.00005835214,0.00005464671,0.003542188,0.00006275388,0.6075423,0.1854422,0.1227932,0.07935829,0.00009853955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005705036,0.001303484,0.984179,0.0001761752,0.0001244756,0.00005887947,0.00003871398,0.0003958252,0.008018527],"genre_scores_gemma":[0.1740797,0.004608115,0.8025786,0.0002899995,0.0004331831,0.0001525439,0.0001818091,0.0001518021,0.01752426],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003187473,"threshold_uncertainty_score":0.01066315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03895820558282735,"score_gpt":0.2845973168722248,"score_spread":0.2456391112893974,"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."}}