{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003302078,0.000686596,0.0002858489,0.0007431439,0.0003292676,0.0004895152,0.0004428103,0.000324785,0.001614265],"category_scores_gemma":[0.001207497,0.0001498944,0.0001518062,0.000468661,0.0001287921,0.0007467467,0.0003678983,0.0004174606,0.0009440733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003639546,"about_ca_system_score_gemma":0.0003244804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001448063,"about_ca_topic_score_gemma":0.00246954,"domain_scores_codex":[0.9996909,0.00004662558,0.00001511364,0.00003287469,0.0001846906,0.00002980513],"domain_scores_gemma":[0.9997002,0.00008476644,0.0000348767,0.00003997193,0.0001277368,0.00001239406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002525285,0.0000994015,0.0008037785,0.000113169,0.00002791286,0.000118683,0.0001687228,0.01842812,0.3735985,0.006282395,0.003469855,0.596637],"study_design_scores_gemma":[0.00005738398,0.0005354208,0.002635883,0.00004636856,0.00009682314,0.0006474095,0.00009611016,0.3260176,0.6302675,0.003175743,0.03635527,0.0000684859],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08402462,0.00183345,0.9029214,0.0001606224,0.0001683104,0.000106365,0.00008663737,0.002987627,0.007710929],"genre_scores_gemma":[0.3471273,0.001435234,0.6441063,0.0001129826,0.0001454606,0.0001032005,0.0004543622,0.0005685329,0.005946623],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001614265,"threshold_uncertainty_score":0.0054003,"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."}}