{"id":"W1892942387","doi":"10.1109/adfsp.1998.685687","title":"Performance evaluation of different reversible decorrelating transforms in the JPEG-2000 baseline system","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Lossless compression; Decorrelation; Lossy compression; JPEG; Computer science; Wavelet transform; JPEG 2000; Transform coding; Lossless JPEG; Filter bank; Baseline (sea); Data compression; Algorithm; Filter (signal processing); Wavelet; Artificial intelligence; Discrete cosine transform; Computer vision; Image compression; Image processing; Image (mathematics); Geology","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":[0.003868614,0.00008201371,0.0001262613,0.00009338308,0.00007358625,0.00003891154,0.0004269937,0.00003321427,0.00006894823],"category_scores_gemma":[0.00005092366,0.00004737626,0.00004565066,0.0003857538,0.00001351012,0.0003517048,0.00002120129,0.0001035811,0.00001664253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008092482,"about_ca_system_score_gemma":0.00002326026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002836837,"about_ca_topic_score_gemma":0.000009267922,"domain_scores_codex":[0.9983812,0.0004615685,0.0002887289,0.0001587446,0.0005556158,0.0001541692],"domain_scores_gemma":[0.9993054,0.0002146386,0.00006082838,0.0003035451,0.00009651006,0.00001904555],"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":[0.00001028003,0.0001424007,0.002946415,0.0001245411,0.00001007727,0.000004478899,0.009472771,0.004920138,0.001595328,0.003021725,0.000309405,0.9774424],"study_design_scores_gemma":[0.0005523345,0.00005218486,0.00343073,0.00008808105,0.0000122077,0.00001195262,0.0001786893,0.9919199,0.003580134,0.0000749949,0.00003199364,0.00006682381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4166333,0.0001792755,0.5545068,0.0002235039,0.0001552158,0.0002496799,2.996723e-7,0.00003544956,0.02801656],"genre_scores_gemma":[0.9845442,0.00001320557,0.01503542,0.00008091194,0.00002156062,0.00001015715,5.969522e-7,0.000003009643,0.0002909618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9869998,"threshold_uncertainty_score":0.1931948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05030831809852353,"score_gpt":0.27674854584586,"score_spread":0.2264402277473365,"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."}}