{"id":"W2128482956","doi":"10.1109/iscas.1997.612859","title":"Lossless compression of color images using an improved integer-based nonlinear wavelet transform","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Chrominance; Lossless compression; RGB color model; Artificial intelligence; Wavelet transform; Computer vision; Data compression; Image compression; Wavelet; Color image; Mathematics; Computer science; Luminance; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004088563,0.0001641802,0.0002724366,0.0001315855,0.0001111105,0.0001139807,0.0006703827,0.00008026787,0.0001081052],"category_scores_gemma":[0.00002455217,0.0001286939,0.0000976977,0.0003095741,0.00009418897,0.0006371737,0.0000611007,0.000143501,0.000006267573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002670302,"about_ca_system_score_gemma":0.00003996531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001228318,"about_ca_topic_score_gemma":0.000005167858,"domain_scores_codex":[0.998671,0.000157903,0.000332893,0.0003274435,0.000238497,0.0002722594],"domain_scores_gemma":[0.9990181,0.0001282793,0.00009716132,0.0004790414,0.000173088,0.0001043533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004709856,0.0005432027,0.00001660083,0.00006152013,0.00001266273,0.00002527277,0.0005100266,0.0005881533,0.8349565,0.0002974893,0.0001475251,0.1627939],"study_design_scores_gemma":[0.0005818333,0.0001531314,0.00002615644,0.00002600869,0.000005787555,0.000006046854,0.00001244485,0.6069098,0.391904,0.0001274386,0.0001361683,0.0001111405],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05691868,0.00005488929,0.9407606,0.0002375324,0.0001515282,0.0001616376,0.000006244637,0.0001063567,0.001602547],"genre_scores_gemma":[0.3982881,0.000002894101,0.6012027,0.0002481553,0.00003463922,0.000001865247,0.000002095779,0.00001014936,0.0002093638],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6063216,"threshold_uncertainty_score":0.5247986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05397898984406609,"score_gpt":0.3054688864615233,"score_spread":0.2514898966174572,"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."}}