{"id":"W1550408533","doi":"10.1109/icip.1999.817177","title":"Image compression based on multi-scale edge compensation","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Computer vision; Image compression; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Coding (social sciences); Data compression; Compensation (psychology); Edge detection; Compression (physics); Image (mathematics); Scale (ratio); Image processing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001645897,0.0003014679,0.0002963094,0.0003222724,0.0001053145,0.0002868669,0.0004160161,0.0003322281,0.0009874117],"category_scores_gemma":[0.000285017,0.0001091664,0.0002151422,0.0002705728,0.0002758072,0.0007569693,0.0002849574,0.0004159785,0.0003778744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000163621,"about_ca_system_score_gemma":0.0001419268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005635733,"about_ca_topic_score_gemma":0.0006877545,"domain_scores_codex":[0.999864,0.00001179422,0.000005685575,0.00002280326,0.00008271016,0.00001298117],"domain_scores_gemma":[0.9998941,0.00002537074,0.00001321418,0.00003034086,0.00003041412,0.000006564056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000420091,0.00009752539,0.001455249,0.0001776553,0.00004434116,0.0004501553,0.0001119006,0.07429765,0.5294159,0.04333047,0.002407138,0.3477918],"study_design_scores_gemma":[0.00003795212,0.0002773457,0.00201416,0.00001709654,0.00002458908,0.00069916,0.00002794781,0.7822679,0.1992275,0.005695287,0.009680744,0.0000301827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07530633,0.0006249592,0.9181417,0.0001566401,0.0001137455,0.00006773839,0.00006657344,0.001111882,0.004410435],"genre_scores_gemma":[0.6215513,0.0009716148,0.3709444,0.0002082415,0.00008429017,0.0000601517,0.0002389979,0.00008857645,0.005852405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009874117,"threshold_uncertainty_score":0.00330323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02630223575907427,"score_gpt":0.2996222729759516,"score_spread":0.2733200372168774,"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."}}