{"id":"W2895868297","doi":"10.1145/3240508.3240643","title":"End-to-End Blind Quality Assessment of Compressed Videos Using Deep Neural Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Codec; Artificial intelligence; Convolutional neural network; Feature extraction; Feature (linguistics); Benchmark (surveying); Pattern recognition (psychology); Extractor; Task (project management); Artificial neural network; Source code; Code (set theory); Probabilistic logic","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":[0.001334443,0.0002348733,0.0004260885,0.0001467092,0.0002120101,0.0002663123,0.001145164,0.000087909,0.0002633076],"category_scores_gemma":[0.0000400842,0.0002102828,0.0001325985,0.0006236717,0.0001515732,0.0007126118,0.0007693699,0.0002008641,0.00001252559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009205328,"about_ca_system_score_gemma":0.0001223613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008538663,"about_ca_topic_score_gemma":0.0002063186,"domain_scores_codex":[0.9969484,0.0004821883,0.0007656251,0.0005883838,0.0006939982,0.0005214163],"domain_scores_gemma":[0.997691,0.000320626,0.000275814,0.001112234,0.000385134,0.0002152404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003196271,0.003104768,0.03441956,0.000375222,0.0006735809,0.00008968415,0.006763011,0.1302058,0.07669374,0.4287988,0.003574022,0.3149823],"study_design_scores_gemma":[0.0006486581,0.0002308355,0.02049629,0.00001808287,0.00001484968,0.000005652971,0.00009645517,0.9723218,0.00508505,0.0004383037,0.0003630791,0.0002809402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07042015,0.00002354018,0.9246072,0.0007198189,0.0005827203,0.0003193791,0.000002808801,0.0001105384,0.003213808],"genre_scores_gemma":[0.7966367,0.000001109148,0.2013486,0.00171459,0.0002134898,0.00000792758,0.000003433032,0.00001016488,0.00006394285],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8421161,"threshold_uncertainty_score":0.8575083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1144728405249392,"score_gpt":0.4143075436738868,"score_spread":0.2998347031489476,"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."}}