{"id":"W2070225802","doi":"10.1117/12.481588","title":"<title>Fast embedded image coding technique using wavelet difference reduction</title>","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Decoding methods; Algorithm; Entropy (arrow of time); Computational complexity theory; Bitstream; Coding (social sciences); Entropy encoding; Artificial intelligence; 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":[0.0004274737,0.0002030681,0.0002445046,0.0001042764,0.00008824401,0.0001689954,0.0008874018,0.0001301529,0.00008397594],"category_scores_gemma":[0.0002398796,0.0001766058,0.0002950011,0.0003310365,0.0001369855,0.000424299,0.0001783082,0.0002387161,0.00001564992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001108404,"about_ca_system_score_gemma":0.00001993128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000132943,"about_ca_topic_score_gemma":7.774209e-9,"domain_scores_codex":[0.9985938,4.542282e-8,0.0003629812,0.0003102944,0.0004512535,0.0002816139],"domain_scores_gemma":[0.9988749,0.00005979405,0.0001916565,0.00007755491,0.0007203773,0.00007570518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005002231,0.00003537772,0.000002014303,0.00009167768,0.00005445128,2.492144e-7,0.0001017998,0.000006792957,0.7133771,0.2781363,0.006421411,0.001767825],"study_design_scores_gemma":[0.0008053255,0.0001993109,0.00008681095,0.0005446753,0.0001105299,0.0002027083,0.0001623045,0.4070901,0.5693256,0.007417098,0.01333374,0.0007218309],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8485188,0.000265466,0.06725441,0.001590132,0.001050891,0.0006996568,0.00002540439,0.0003544296,0.08024076],"genre_scores_gemma":[0.08428363,0.0000779505,0.9125792,0.00006347591,0.0004898739,0.00004640926,0.000001976194,0.00004613067,0.00241138],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8453248,"threshold_uncertainty_score":0.7201776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01992547832891065,"score_gpt":0.2492476443932063,"score_spread":0.2293221660642956,"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."}}