{"id":"W2130285405","doi":"10.1109/isit.2006.261850","title":"Rate Distortion Optimization of H.264 with Main Profile Compatibility","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Encoder; Quantization (signal processing); Computer science; Coding (social sciences); Algorithm; Reference software; Rate distortion; Rate–distortion optimization; Rate–distortion theory; Binary number; Residual; Artificial intelligence; Mathematics; Data compression; Multiview Video Coding; Statistics; Arithmetic","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001422021,0.00006510156,0.00009726667,0.00005355834,0.00005462935,0.00002987166,0.0003072296,0.00003196193,0.00002004789],"category_scores_gemma":[0.00001387399,0.00004458058,0.00001836637,0.0002615501,0.0000505078,0.0001932776,0.00008751734,0.0000431608,0.000003239436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002176107,"about_ca_system_score_gemma":0.00002324302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001017486,"about_ca_topic_score_gemma":0.00002079075,"domain_scores_codex":[0.9993836,0.00003852639,0.0001618777,0.0001952753,0.0001240731,0.00009661251],"domain_scores_gemma":[0.9994006,0.00002847361,0.00009922415,0.0003924567,0.00006653257,0.0000127413],"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":[0.00008794876,0.0008226837,0.05578172,0.0001253407,0.00002416818,0.000009899886,0.0002078634,0.5611038,0.01170256,0.2969043,0.01354804,0.05968168],"study_design_scores_gemma":[0.0004461096,0.0002245133,0.03155376,0.00005071683,0.000004821503,0.000004371533,0.00003559259,0.776499,0.1813303,0.009481872,0.000152932,0.000216017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07562292,0.0000152117,0.9208044,0.0002502836,0.00003489321,0.00009687179,0.000001183191,0.0003869765,0.002787209],"genre_scores_gemma":[0.8418171,0.000001079149,0.157776,0.00001192842,0.000004565318,0.000008911208,0.000003539461,0.000002228234,0.0003745771],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7661942,"threshold_uncertainty_score":0.1817943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009642291932054053,"score_gpt":0.2075075619037998,"score_spread":0.1978652699717457,"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."}}