{"id":"W2094938072","doi":"10.1109/ipta.2010.5586733","title":"Temporal transcoding of H.264/AVC video to the scalable format","year":2010,"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":"McGill University","funders":"","keywords":"Computer science; Transcoding; Scalable Video Coding; Macroblock; Motion compensation; Motion vector; Quarter-pixel motion; Block-matching algorithm; Bitstream; Motion estimation; Data compression; Codec; Real-time computing; Scalability; Multiview Video Coding; Encoding (memory); Pixel; Video compression picture types; Coding (social sciences); Computer vision; Decoding methods; Computer hardware; Artificial intelligence; Algorithm; Video processing; Video tracking; Computer network; Image (mathematics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001657237,0.0002557613,0.0001584268,0.0002676504,0.0001260104,0.0002313227,0.0002934025,0.0001762687,0.001356593],"category_scores_gemma":[0.0004838684,0.00008058036,0.0002363045,0.0002244756,0.0001312712,0.0002871105,0.0001822415,0.0002801848,0.0003333029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000142031,"about_ca_system_score_gemma":0.000285965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001655302,"about_ca_topic_score_gemma":0.00340535,"domain_scores_codex":[0.9999121,0.000007357642,0.000004474674,0.00001151996,0.00005477937,0.000009633484],"domain_scores_gemma":[0.9998227,0.00003191747,0.00001373915,0.00003511187,0.00008808756,0.000008505535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001336976,0.00005458814,0.0006734484,0.0001510468,0.00004067827,0.0006657615,0.00008476996,0.01953453,0.7163854,0.007226171,0.003303229,0.2517467],"study_design_scores_gemma":[0.00004495185,0.0005039838,0.005468073,0.00004614913,0.00007547989,0.002132189,0.0001584093,0.4679866,0.4952647,0.005485553,0.02279253,0.00004144279],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1576203,0.0006675061,0.8291028,0.0001642984,0.0002273047,0.0001466838,0.0002816411,0.001383892,0.01040556],"genre_scores_gemma":[0.5388574,0.0008944875,0.4495388,0.000168673,0.0001683453,0.00008194665,0.0008486268,0.000151421,0.009290264],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001655302,"threshold_uncertainty_score":0.004538298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01557283116031654,"score_gpt":0.2417599734600293,"score_spread":0.2261871422997128,"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."}}