{"id":"W2952144652","doi":"10.1109/tip.2020.3001405","title":"Modeling Generalized Rate-Distortion Functions","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Distortion function; Encoder; Distortion (music); Computer science; Computation; Algorithm; Parametric statistics; Encoding (memory); Function (biology); Interpolation (computer graphics); Sampling (signal processing); Probabilistic logic; Measure (data warehouse); Monotonic function; Video quality; Artificial intelligence; Mathematics; Data mining; Computer vision; Statistics; Image (mathematics); Decoding methods; Metric (unit); Bandwidth (computing)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002961586,0.001392314,0.0009908255,0.00151117,0.0002447743,0.001263037,0.00189386,0.001273594,0.0007252541],"category_scores_gemma":[0.01236738,0.0004807482,0.0009316248,0.001601395,0.0008238225,0.001961785,0.0008381224,0.001295303,0.0004201088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00114624,"about_ca_system_score_gemma":0.000625457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004884458,"about_ca_topic_score_gemma":0.002855229,"domain_scores_codex":[0.9985144,0.0004862055,0.00008656911,0.0003329165,0.0004660118,0.0001139757],"domain_scores_gemma":[0.9950913,0.003022468,0.0005931815,0.0007103756,0.0005029891,0.00007962528],"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":[0.00007803159,0.00003428314,0.001444827,0.00009214206,0.00003565066,0.0001150559,0.00007288418,0.9358954,0.004211605,0.02156624,0.0004815715,0.03597239],"study_design_scores_gemma":[0.000002113796,0.00001135655,0.0001980448,0.000002963857,0.000003705482,0.00004038355,0.00000403555,0.9957768,0.0006065264,0.003075135,0.0002725326,0.000006396994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01878204,0.0004192936,0.9798136,0.00007141113,0.00001117783,0.00005179816,0.0001690768,0.0002235146,0.0004579448],"genre_scores_gemma":[0.7060999,0.001642808,0.287846,0.0001177439,0.00007997238,0.0003204602,0.001062304,0.0001576033,0.002673203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004884458,"threshold_uncertainty_score":0.01566255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05200066798194278,"score_gpt":0.2998667507708233,"score_spread":0.2478660827888805,"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."}}