{"id":"W2048319722","doi":"10.1109/tip.2011.2171352","title":"Edge-Based Perceptual Image Coding","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Codec; Artificial intelligence; Computer vision; Coding (social sciences); Computer science; Edge detection; Enhanced Data Rates for GSM Evolution; Image quality; Residual; Image processing; Mathematics; Pattern recognition (psychology); Algorithm; Image (mathematics); Statistics; Telecommunications","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":[0.0001969526,0.0003752398,0.0002570229,0.0005163213,0.0001611066,0.0004404754,0.000731166,0.0004234209,0.002044379],"category_scores_gemma":[0.001037151,0.0001237854,0.0002164731,0.0004521561,0.0004071915,0.0008053622,0.0006703892,0.0007183915,0.000649187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001999168,"about_ca_system_score_gemma":0.0002553641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006442146,"about_ca_topic_score_gemma":0.0008879428,"domain_scores_codex":[0.9998032,0.00002491506,0.000008596337,0.00002865547,0.0001142784,0.00002031913],"domain_scores_gemma":[0.9995453,0.0001103613,0.00004286242,0.000100701,0.0001799403,0.00002077357],"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":[0.0005107246,0.00008760028,0.0004400188,0.0002708075,0.00002987815,0.000226211,0.0001121996,0.08569749,0.4509225,0.05868787,0.003502306,0.3995124],"study_design_scores_gemma":[0.00004355484,0.0002432969,0.0007725878,0.00004134739,0.00003156542,0.0004518012,0.00002159461,0.7894184,0.1804546,0.01630011,0.01216222,0.00005899087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01875902,0.000388483,0.9771405,0.00008405749,0.00005086053,0.0000383246,0.000097234,0.0003783247,0.003063032],"genre_scores_gemma":[0.3392429,0.0008412239,0.6539995,0.0002656057,0.00009214236,0.00009233422,0.0003308154,0.0001688276,0.004966657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002044379,"threshold_uncertainty_score":0.006839156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04713788817267429,"score_gpt":0.2880777119637266,"score_spread":0.2409398237910523,"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."}}