{"id":"W2103538377","doi":"10.1109/icip.1997.638618","title":"Efficient RD optimized macroblock coding mode selection for MPEG-2 video encoding","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Macroblock; Computer science; Encoder; Coding tree unit; Data compression; Context-adaptive binary arithmetic coding; Coding (social sciences); Rate–distortion theory; Multiview Video Coding; Bitstream; Context-adaptive variable-length coding; Algorithm; Real-time computing; Artificial intelligence; Decoding methods; Video tracking; Video processing; Mathematics","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":[0.0002221974,0.0001853711,0.0002205542,0.0002244973,0.0004349435,0.0002468232,0.000865117,0.0001131397,0.00005794655],"category_scores_gemma":[0.0001431374,0.0001574549,0.0001240886,0.0004891851,0.00003140709,0.0001488489,0.0002971161,0.0001564357,0.00004651978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000772511,"about_ca_system_score_gemma":0.00001263139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001753883,"about_ca_topic_score_gemma":0.000002339343,"domain_scores_codex":[0.9984679,0.0000338633,0.0002894739,0.0005274496,0.0002407781,0.0004405487],"domain_scores_gemma":[0.999063,0.000212189,0.0001063039,0.0004386686,0.0001098015,0.00007004595],"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":[0.00004871325,0.0003912305,0.0001295036,0.00008547717,0.00007499688,0.000006796925,0.001165371,0.4043868,0.1172333,0.2025704,0.07324981,0.2006576],"study_design_scores_gemma":[0.0005625674,0.00007376843,0.000004173977,0.00003901985,0.000005462883,0.00001600418,0.00003897861,0.9196746,0.07619408,0.001347556,0.001835098,0.0002086774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01002514,0.0001095486,0.9801408,0.002112326,0.0003579098,0.0002838093,0.000001224964,0.002008561,0.004960669],"genre_scores_gemma":[0.7865832,0.00002735723,0.2112479,0.0001643829,0.00003659312,0.00008577223,4.326378e-7,0.00001042001,0.001843969],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.776558,"threshold_uncertainty_score":0.6420823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03247059254068735,"score_gpt":0.2615823726203392,"score_spread":0.2291117800796519,"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."}}