{"id":"W1644173899","doi":"10.1109/lsp.2015.2487369","title":"A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images","year":2015,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":426,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Contrast (vision); Artificial intelligence; Computer science; Image quality; Representation (politics); Quality (philosophy); Pattern recognition (psychology); Computer vision; Perception; Feature (linguistics); Image (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":[0.0007082644,0.000495514,0.0005381893,0.00181018,0.0002307033,0.0007109257,0.0006495416,0.0005210504,0.001975595],"category_scores_gemma":[0.002659879,0.0002064008,0.0006802165,0.001179923,0.0003679489,0.0009827188,0.0005476319,0.0007934043,0.0006114758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004484192,"about_ca_system_score_gemma":0.0004049852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001726872,"about_ca_topic_score_gemma":0.001774167,"domain_scores_codex":[0.9995525,0.00006284978,0.00002729571,0.0001048829,0.0002229666,0.00002947629],"domain_scores_gemma":[0.99914,0.0001805995,0.000119495,0.0001544773,0.0003659352,0.0000395254],"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":[0.0003539194,0.000128119,0.00385583,0.0002910591,0.000168784,0.0001498881,0.0001706401,0.04658001,0.1734283,0.007127689,0.003643974,0.7641017],"study_design_scores_gemma":[0.00003804189,0.0002929358,0.0101428,0.00002385528,0.0001149334,0.0006381632,0.00005228361,0.91838,0.0625629,0.002858755,0.004827604,0.00006768389],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01914457,0.0002528875,0.9789533,0.00005133367,0.00003235926,0.00008459183,0.000129197,0.0006078499,0.0007439085],"genre_scores_gemma":[0.270332,0.0004089695,0.7266211,0.00006675712,0.00005560368,0.0001493972,0.0004864203,0.0001816704,0.00169802],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001975595,"threshold_uncertainty_score":0.006609023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1106064119134101,"score_gpt":0.4258384142188925,"score_spread":0.3152320023054824,"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."}}