{"id":"W2097448786","doi":"10.1109/icme.2003.1221272","title":"Multiple arbitrary shape ROI coding with zerotree based wavelet coders","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Coding (social sciences); Computer vision; Region of interest; Context-adaptive binary arithmetic coding; Coding tree unit; Pixel; Context-adaptive variable-length coding; Tunstall coding; Data compression; Sub-band coding; Transform coding; JPEG; Wavelet; Image compression; Algorithm; Decoding methods; Mathematics; Image processing; Speech recognition; Image (mathematics); Speech coding; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001730699,0.0001971016,0.000177571,0.0001169792,0.000142202,0.0001062327,0.0008686771,0.00005525695,0.0001191074],"category_scores_gemma":[0.00008380906,0.0001497762,0.00003931991,0.000360707,0.00006108732,0.0008641137,0.0001497764,0.0001809525,0.00002878915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003365924,"about_ca_system_score_gemma":0.00008986806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001224878,"about_ca_topic_score_gemma":0.00001129861,"domain_scores_codex":[0.9985343,0.00007675843,0.0001995648,0.0005222798,0.0003232301,0.000343829],"domain_scores_gemma":[0.9985302,0.0002260154,0.00008393898,0.0009567602,0.00006523117,0.0001378277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001387465,0.0008130879,0.01075178,0.00009843994,0.00007809028,0.0004323907,0.0003489273,0.00143095,0.04850324,0.7212206,0.04919982,0.1669839],"study_design_scores_gemma":[0.001079236,0.0001673595,0.0004138787,0.00007204008,0.000004277431,0.00003214622,0.00002768421,0.5893526,0.371022,0.004695683,0.03259378,0.0005393645],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001317374,0.00002399218,0.9859155,0.0002583238,0.00005389857,0.0002557839,0.000005694779,0.001076933,0.01109244],"genre_scores_gemma":[0.3271295,0.000004585519,0.6714182,0.001257814,0.000006327062,0.00002910767,0.000005870926,0.00001379629,0.0001348541],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.716525,"threshold_uncertainty_score":0.6107698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01860493553005252,"score_gpt":0.2374440941971144,"score_spread":0.2188391586670619,"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."}}