{"id":"W2319394204","doi":"10.5594/j05313","title":"Improving MPEG Performance Using Frame Partitioning","year":2000,"lang":"en","type":"article","venue":"SMPTE Journal","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Uncompressed video; MPEG-2; Computer vision; Data compression; Artificial intelligence; Coding (social sciences); Decoding methods; Multiview Video Coding; MPEG-4; Block (permutation group theory); Frame (networking); Segmentation; Image compression; Encoding (memory); Frame rate; Image processing; Video tracking; Real-time computing; Video processing; Image (mathematics); Algorithm; Mathematics; Telecommunications","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":[0.0002709567,0.00009912958,0.0001084783,0.0001050736,0.0006184567,0.000433303,0.000717784,0.00006297373,0.0002028169],"category_scores_gemma":[0.00002872024,0.00008284234,0.00005867855,0.0002274231,0.00003497752,0.0008526075,0.0001173579,0.0004363364,0.00007148079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004650982,"about_ca_system_score_gemma":0.00006282113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006848245,"about_ca_topic_score_gemma":2.898437e-7,"domain_scores_codex":[0.9990253,0.00003243985,0.0002233263,0.0001713925,0.0002402359,0.0003073193],"domain_scores_gemma":[0.9994492,0.00002125373,0.0001122672,0.0002912344,0.00005135749,0.00007470947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004721915,0.00001920269,0.001667932,0.000006770909,0.000008329151,0.00002482685,0.0001847101,0.004085288,0.00429556,0.0005856531,0.0004465317,0.9886705],"study_design_scores_gemma":[0.0004890483,0.0001793509,0.003589296,0.000329251,0.0000112479,0.001598334,0.0000875373,0.9545653,0.02164425,0.006972448,0.01012177,0.0004121262],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7772632,0.0002894612,0.2200926,0.0003763549,0.0003357543,0.00002634823,1.804997e-7,0.0002832808,0.001332839],"genre_scores_gemma":[0.9452077,0.0001336108,0.05402685,0.000159456,0.0001427754,0.000001489879,8.007385e-8,0.00000609376,0.000321985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9882584,"threshold_uncertainty_score":0.4756732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02177194483955806,"score_gpt":0.2365169982349727,"score_spread":0.2147450533954147,"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."}}