{"id":"W4226133466","doi":"10.1109/access.2022.3167446","title":"No-Reference Video Quality Assessment Using Distortion Learning and Temporal Attention","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Video quality; Distortion (music); Subjective video quality; Artificial intelligence; Deep learning; Quality (philosophy); Video processing; Task (project management); Machine learning; Multimedia; Image quality; Computer vision; Image (mathematics); Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001032653,0.0008829538,0.0009462279,0.001276827,0.0002273538,0.0008951928,0.001372971,0.0006925717,0.0009390379],"category_scores_gemma":[0.003848387,0.0002551835,0.0007260407,0.0006120192,0.0004644328,0.001208035,0.0008993642,0.001123924,0.0003467757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001101387,"about_ca_system_score_gemma":0.0006711917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008300998,"about_ca_topic_score_gemma":0.007558654,"domain_scores_codex":[0.999216,0.00008913224,0.00004866218,0.0002394949,0.0003287341,0.00007791702],"domain_scores_gemma":[0.9988544,0.0002980279,0.0001712547,0.0001171794,0.0004838158,0.0000753381],"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":[0.0005321726,0.0002587805,0.005966591,0.0001839342,0.0001826222,0.0002403997,0.0001081614,0.182648,0.02687412,0.001838725,0.003159532,0.778007],"study_design_scores_gemma":[0.0000144757,0.0001100236,0.002422433,0.00001533988,0.0000368302,0.0001689797,0.00001950214,0.9861218,0.009395977,0.001062383,0.0006156373,0.00001653158],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1100512,0.002374685,0.8823367,0.0003187141,0.0001235388,0.0001561025,0.0002089069,0.001534285,0.002895862],"genre_scores_gemma":[0.8598412,0.0009954912,0.134547,0.0002942367,0.0001275048,0.00006465615,0.0006345381,0.0001191535,0.003376223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008300998,"threshold_uncertainty_score":0.01650536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1323431878571834,"score_gpt":0.4307362988398952,"score_spread":0.2983931109827119,"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."}}