{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004235155,0.00006837437,0.0001162679,0.00005579269,0.00008652547,0.00005687648,0.0001096514,0.00001961384,0.000007667584],"category_scores_gemma":[0.00004033186,0.00005572753,0.00004019439,0.00007020438,0.00003432658,0.0002481111,0.00003542348,0.00003745836,8.306061e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007642825,"about_ca_system_score_gemma":0.00001786102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005021459,"about_ca_topic_score_gemma":9.451239e-8,"domain_scores_codex":[0.9993355,0.0001163519,0.0001306081,0.0001975099,0.000109496,0.0001105899],"domain_scores_gemma":[0.999531,0.0001568583,0.00004608501,0.00014343,0.00008353122,0.00003908894],"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":[0.0001026653,0.0004906803,0.0001103098,0.0001901062,0.00004635071,0.000008389427,0.0003869191,0.00004911022,0.6102659,0.2637779,0.01145329,0.1131184],"study_design_scores_gemma":[0.001259829,0.0001363623,0.0007626593,0.0000215763,0.000009959354,0.0001873859,0.0000130288,0.1264523,0.8288419,0.03967536,0.002456334,0.0001832813],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009909297,0.00005910257,0.9767799,0.00005085557,0.00008407062,0.0001283089,0.000003212818,0.00003549299,0.01294979],"genre_scores_gemma":[0.1890318,0.000003463393,0.8106454,0.00007915912,0.00001298136,0.000008870966,0.000004318883,0.000003494649,0.0002104888],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2241025,"threshold_uncertainty_score":0.2272503,"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."}}