{"id":"W2091659514","doi":"10.1109/tip.2006.888341","title":"Adaptive Directional Lifting-Based Wavelet Transform for Image Coding","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":218,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Wavelet transform; Computer vision; Lifting scheme; Wavelet; Computer science; Second-generation wavelet transform; Transform coding; Pixel; Discrete wavelet transform; Image resolution; Coding (social sciences); Stationary wavelet transform; Mathematics; Pattern recognition (psychology); Image (mathematics); Discrete cosine transform","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":[0.0002184571,0.0003503766,0.0002516329,0.0004676601,0.0001557781,0.000357955,0.0003652852,0.0003741318,0.001193469],"category_scores_gemma":[0.0006138123,0.000120542,0.0002910785,0.0007715388,0.0002527575,0.0004802782,0.0004351145,0.0006250928,0.0005785832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002192319,"about_ca_system_score_gemma":0.0002535417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004710258,"about_ca_topic_score_gemma":0.0005179128,"domain_scores_codex":[0.9998609,0.00002702781,0.00000845841,0.00001613304,0.00007449746,0.00001294762],"domain_scores_gemma":[0.9998778,0.00003174471,0.00001402766,0.00003207856,0.00003605599,0.00000825301],"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":[0.000148065,0.00005895694,0.0004647969,0.0002478506,0.00002169294,0.0002485461,0.0001169111,0.05009619,0.2516882,0.07558744,0.005005496,0.6163158],"study_design_scores_gemma":[0.00003233566,0.0001556817,0.000658292,0.00005039147,0.00002302294,0.0006351078,0.00002504599,0.8791407,0.07197547,0.02023731,0.02702551,0.00004123373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008968479,0.0006488197,0.9881619,0.0001111885,0.0000750093,0.00002966201,0.0000401229,0.0001717008,0.001793127],"genre_scores_gemma":[0.1963863,0.00183783,0.7974892,0.0001863829,0.0001540746,0.0001251948,0.0002371951,0.00006176527,0.003522076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001193469,"threshold_uncertainty_score":0.003992558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02839448410608021,"score_gpt":0.3048546841069855,"score_spread":0.2764602000009053,"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."}}