{"id":"W2109761230","doi":"10.1007/978-3-642-13772-3_2","title":"Structural Similarity-Based Approximation of Signals and Images Using Orthogonal Bases","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Orthonormal basis; Similarity (geometry); Representation (politics); Computer science; Algorithm; Basis (linear algebra); Basis function; Approximation error; Simple (philosophy); Image quality; Mean squared error; Scaling; Mathematics; Image (mathematics); Artificial intelligence; Pattern recognition (psychology); Statistics; Mathematical analysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009678873,0.0003963982,0.0005047362,0.0005952614,0.000242606,0.0004389175,0.001321323,0.0002595787,0.00002122765],"category_scores_gemma":[0.0000963744,0.0003552549,0.0001026836,0.000322035,0.0009419216,0.000808484,0.0006944721,0.0006772807,0.000001125061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008182183,"about_ca_system_score_gemma":0.0007036123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003552925,"about_ca_topic_score_gemma":0.00002979839,"domain_scores_codex":[0.9970903,0.00006349224,0.0005551315,0.0009811741,0.0009126031,0.0003973272],"domain_scores_gemma":[0.9978201,0.0004791609,0.0004399845,0.0008042015,0.0003412809,0.0001153174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003414523,0.0001046443,0.0008624528,0.0008118999,0.00004492452,0.0001335915,0.00143361,0.07642194,0.0655797,0.04190323,0.000009828752,0.81266],"study_design_scores_gemma":[0.0002534809,0.000113159,0.0003738188,0.0002868323,0.00001379545,0.00003697897,1.966543e-7,0.8395725,0.0600329,0.09880434,0.0000482062,0.0004637404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003562682,0.0001713577,0.9947372,0.0005086176,0.0004945659,0.0002957913,0.00001786289,0.00005322525,0.0001587054],"genre_scores_gemma":[0.2830337,0.000004031605,0.7159968,0.0007858352,0.0001416868,0.000002165182,0.000006950447,0.00001463597,0.00001419221],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8121963,"threshold_uncertainty_score":0.99989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03713553090442003,"score_gpt":0.3009465212835721,"score_spread":0.2638109903791521,"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."}}