{"id":"W1933924263","doi":"10.1109/pacrim.1997.619959","title":"A comparison of new reversible wavelet transforms for 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":"","keywords":"Image compression; Wavelet transform; Data compression; Computer science; Wavelet; Texture compression; Compression (physics); Artificial intelligence; Transform coding; Computer vision; Algorithm; Image (mathematics); Image processing; Discrete cosine transform; Materials science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007321161,0.0005329872,0.000387813,0.001054113,0.0001828399,0.0005347953,0.0005202626,0.0005277902,0.00198382],"category_scores_gemma":[0.002724616,0.0002125353,0.0004125668,0.0006657512,0.0004674548,0.001167877,0.0003548831,0.0006078975,0.0004540053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002370383,"about_ca_system_score_gemma":0.0002336477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002363962,"about_ca_topic_score_gemma":0.0004776929,"domain_scores_codex":[0.9995025,0.00005535792,0.00002150046,0.0000299464,0.0003524921,0.00003821979],"domain_scores_gemma":[0.9993423,0.0003545755,0.0000559259,0.00007569604,0.0001401427,0.00003149683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001270158,0.0003090706,0.001146184,0.0006538557,0.0001601011,0.0006236464,0.0001394138,0.03644978,0.3206942,0.03583717,0.002392884,0.6003236],"study_design_scores_gemma":[0.000334919,0.002424536,0.005263526,0.0001412891,0.0002569956,0.003861239,0.0001268961,0.4770212,0.4725991,0.01184936,0.02593916,0.0001819007],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2712025,0.01080502,0.6954406,0.000500615,0.0004839824,0.0003592474,0.0002133716,0.001658991,0.01933569],"genre_scores_gemma":[0.6359266,0.008674988,0.3462284,0.0001628245,0.0002811282,0.000242064,0.0004603918,0.0002038418,0.00781977],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00198382,"threshold_uncertainty_score":0.00663656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07174400082135461,"score_gpt":0.35533802837886,"score_spread":0.2835940275575054,"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."}}