{"id":"W4379116867","doi":"10.1109/tbc.2023.3277193","title":"A Deep Learning-Based No-Reference Quality Metric for High-Definition Images Compressed With HEVC","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Metric (unit); Video quality; Computer vision; Data compression; Compression artifact; Coding (social sciences); Image quality; Transform coding; Image compression; Image processing; Image (mathematics); Mathematics; Discrete cosine transform","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0007589678,0.0002527249,0.0003018613,0.0004591405,0.0007062812,0.0002902734,0.0004358758,0.0000841255,0.00002648001],"category_scores_gemma":[0.00008155594,0.0002356515,0.0001115214,0.001542285,0.00006177838,0.0005316815,0.00000525416,0.0003962997,0.0001749076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008856935,"about_ca_system_score_gemma":0.000110488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002704098,"about_ca_topic_score_gemma":0.00004252531,"domain_scores_codex":[0.9976717,0.0002934716,0.0004209177,0.0006098466,0.000511106,0.0004929599],"domain_scores_gemma":[0.9971704,0.001698053,0.0002106313,0.0004498915,0.0003623875,0.00010859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003402741,0.0007088924,0.00007954006,0.0005192093,0.0001413426,0.00003350369,0.000670747,0.8146178,0.01061683,0.001835686,0.0002260161,0.1702102],"study_design_scores_gemma":[0.003131829,0.001331337,0.002317239,0.0002242216,0.00007214494,0.000008873361,0.0002557362,0.9235407,0.06722659,0.0006229193,0.00048738,0.0007809972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006136454,0.00001378785,0.9914585,0.0004374057,0.0003082428,0.0003841843,0.00002887879,0.0008774162,0.0003551814],"genre_scores_gemma":[0.9251544,0.000007866308,0.07403379,0.0002392089,0.00004309433,0.0002306612,0.00002583338,0.00002639548,0.0002387575],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9190179,"threshold_uncertainty_score":0.9609588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08430370174334498,"score_gpt":0.3265863788771651,"score_spread":0.2422826771338201,"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."}}