{"id":"W2950177980","doi":"10.1016/j.image.2014.09.010","title":"Sparse representation-based image quality assessment","year":2014,"lang":"en","type":"article","venue":"Signal Processing Image Communication","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Qatar National Research Fund","keywords":"Basis (linear algebra); Computer science; Artificial intelligence; Metric (unit); Pattern recognition (psychology); Sparse approximation; Representation (politics); Image (mathematics); Image quality; Set (abstract data type); Quality (philosophy); Computer vision; 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":[0.001406573,0.0005579373,0.0006616426,0.001929015,0.0001987608,0.001096523,0.0006230096,0.0008222468,0.001826496],"category_scores_gemma":[0.004957717,0.0002145461,0.0005667015,0.001073323,0.000461121,0.001326802,0.0009545592,0.0006827966,0.000490065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004309324,"about_ca_system_score_gemma":0.0004351274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001370363,"about_ca_topic_score_gemma":0.00134638,"domain_scores_codex":[0.9990283,0.0002145327,0.00004838015,0.0001251914,0.0005194386,0.00006416099],"domain_scores_gemma":[0.9980285,0.0005454581,0.0002641929,0.0001914726,0.0008937769,0.00007662385],"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":[0.0009455127,0.0002450812,0.006286023,0.0004124376,0.0002247928,0.0001765437,0.0001373607,0.1096257,0.1616481,0.007951096,0.002840689,0.7095067],"study_design_scores_gemma":[0.00002667692,0.0002243199,0.005222468,0.00002539824,0.00008438898,0.000339756,0.00004504009,0.9576892,0.0324369,0.002828222,0.001044953,0.00003270265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03573507,0.0003681543,0.961874,0.0001458653,0.00003273834,0.00006693735,0.0001243764,0.0003744155,0.001278388],"genre_scores_gemma":[0.6480538,0.0009296756,0.3477011,0.0001286404,0.0001107777,0.00008532733,0.0005969413,0.0001421835,0.00225155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001929015,"threshold_uncertainty_score":0.007438779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07733434993029878,"score_gpt":0.4017951334646796,"score_spread":0.3244607835343808,"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."}}