{"id":"W2075116490","doi":"10.1109/cjece.2006.259203","title":"Image compression with a multiresolution neural network","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Image compression; Artificial neural network; Computer science; Artificial intelligence; Compression (physics); Data compression; Multiresolution analysis; Computer vision; Image (mathematics); Pattern recognition (psychology); Image processing; Wavelet; Wavelet transform; Materials science; Discrete wavelet transform","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00008202579,0.0001244539,0.0001729191,0.0001911745,0.00009522367,0.0001364264,0.0003608729,0.00003983441,0.000001349383],"category_scores_gemma":[0.00000875446,0.00009646316,0.00003350813,0.0003346926,0.00002243252,0.0004758336,0.00004644139,0.0002528019,4.031947e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005312698,"about_ca_system_score_gemma":0.00008440874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002415789,"about_ca_topic_score_gemma":0.00007959147,"domain_scores_codex":[0.9991434,0.00002163218,0.0002247322,0.0001470527,0.0001280896,0.0003351171],"domain_scores_gemma":[0.9992986,0.00007598346,0.00009447843,0.0001446483,0.00009047621,0.0002958259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000063881,0.00009655281,0.008236233,0.0000717201,0.00007565781,0.002374142,0.0001425198,0.6123548,0.006932192,0.06678225,0.03027924,0.2725909],"study_design_scores_gemma":[0.0002436239,0.0002054252,0.01373154,0.00008883963,0.000003999114,0.0005397574,1.955956e-7,0.9796913,0.0003417329,0.0005030071,0.004505142,0.000145482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01419194,0.0008923229,0.9845329,0.00011383,0.0001317001,0.00005317287,7.527829e-7,0.00005607134,0.00002730547],"genre_scores_gemma":[0.5878807,0.000005504578,0.4117737,0.00005859854,0.0002674474,0.000001222387,9.675621e-7,0.000007713158,0.000004068943],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5736888,"threshold_uncertainty_score":0.3933654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003554565684909997,"score_gpt":0.1752169863300073,"score_spread":0.1716624206450973,"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."}}