{"id":"W1723956339","doi":"10.1109/icosp.1998.770329","title":"SAR image compression based on the discrete wavelet transform","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Texture compression; Wavelet; Image compression; Artificial intelligence; Computer science; Wavelet transform; Data compression; Computer vision; Discrete wavelet transform; Compression (physics); Wavelet packet decomposition; Image texture; Pattern recognition (psychology); Image segmentation; Image (mathematics); Image processing; Materials science","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":[0.0001386357,0.0002820771,0.000305156,0.0004329232,0.0001169555,0.0002731083,0.000237573,0.0002457931,0.001061936],"category_scores_gemma":[0.000493758,0.00009186887,0.0001916094,0.0004894709,0.0003384197,0.0004520962,0.0002427125,0.0003905668,0.0004373584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009650611,"about_ca_system_score_gemma":0.0001547542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002168604,"about_ca_topic_score_gemma":0.0001949008,"domain_scores_codex":[0.9998528,0.00001712926,0.000006907224,0.00001419819,0.0001002954,0.000008680323],"domain_scores_gemma":[0.999885,0.00005154918,0.000009058987,0.00001903406,0.00003005728,0.000005290026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001999591,0.00006790221,0.0004222338,0.0002816918,0.00002615226,0.0003482936,0.00006694294,0.0195416,0.3525712,0.0324093,0.00250639,0.5915582],"study_design_scores_gemma":[0.0001195392,0.0005615937,0.004216225,0.00008576435,0.00007022569,0.002512914,0.00006666061,0.5230805,0.4035359,0.02900566,0.03669138,0.000053682],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06223978,0.002312654,0.9261968,0.0003330598,0.0002897163,0.0001046334,0.0001148702,0.0007771022,0.007631463],"genre_scores_gemma":[0.4938482,0.006874992,0.4876623,0.0001985361,0.0004406984,0.0001147808,0.0005119177,0.000144755,0.01020389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001061936,"threshold_uncertainty_score":0.003552556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02166150256778859,"score_gpt":0.259205500391752,"score_spread":0.2375439978239634,"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."}}