{"id":"W2100780594","doi":"10.1109/ccece.1999.808039","title":"A hybrid approach of wavelet packet and directional decomposition for image compression","year":2003,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Wavelet packet decomposition; Wavelet transform; Wavelet; Stationary wavelet transform; Computer science; Second-generation wavelet transform; Artificial intelligence; Image compression; Discrete wavelet transform; Lifting scheme; Computer vision; Data compression; Pattern recognition (psychology); Algorithm; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003680694,0.0005000186,0.000483342,0.000939191,0.0001889089,0.0005261961,0.0006620722,0.0005031837,0.0013355],"category_scores_gemma":[0.0006198023,0.0001884803,0.0004483681,0.001084278,0.0002661907,0.0008239539,0.000459052,0.0007092381,0.0007297933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001949148,"about_ca_system_score_gemma":0.0002690579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005027058,"about_ca_topic_score_gemma":0.0006250482,"domain_scores_codex":[0.9997476,0.00003265718,0.0000157292,0.00002918362,0.0001560342,0.00001887009],"domain_scores_gemma":[0.9998314,0.00004522824,0.000009856411,0.00003356152,0.0000715113,0.000008380071],"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":[0.0001489043,0.00009482132,0.0003536805,0.0002684707,0.00006542762,0.0001689087,0.0000576711,0.02414847,0.1072919,0.04692508,0.003424214,0.8170526],"study_design_scores_gemma":[0.00006532274,0.0003361941,0.0008728807,0.00005754945,0.0001128678,0.00111343,0.00005130939,0.8357075,0.09490457,0.01735445,0.04936194,0.00006199319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003234516,0.0006238573,0.9941941,0.00007817983,0.0001000801,0.00003944633,0.0000228297,0.0002667387,0.001440236],"genre_scores_gemma":[0.09039585,0.003133162,0.9006442,0.0001823831,0.0002060356,0.0001393673,0.0002151014,0.0001011204,0.004982821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0013355,"threshold_uncertainty_score":0.004467726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01968459262657439,"score_gpt":0.295077546184662,"score_spread":0.2753929535580876,"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."}}