{"id":"W2099481393","doi":"10.1109/isce.2011.5973795","title":"New method for concealing entirely lost frames in H.264 video transmission over wireless networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada; Innovation, Science and Economic Development Canada","funders":"","keywords":"Computer science; Motion compensation; Motion vector; Computer vision; Extrapolation; Quarter-pixel motion; Artificial intelligence; Error concealment; Block (permutation group theory); Transmission (telecommunications); Compensation (psychology); Block-matching algorithm; Blocking (statistics); Motion estimation; Motion (physics); Wireless; Video processing; Decoding methods; Algorithm; Computer network; Image (mathematics); Video tracking; Telecommunications; 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":[0.0003807721,0.0004040633,0.0002767269,0.0006389481,0.0002810061,0.0003350971,0.0004890453,0.0004198482,0.001003353],"category_scores_gemma":[0.0005798031,0.0001998297,0.0002845562,0.000294866,0.0002672488,0.0008483867,0.0003673146,0.0005270615,0.0003121747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002899368,"about_ca_system_score_gemma":0.0004700746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001463863,"about_ca_topic_score_gemma":0.002236067,"domain_scores_codex":[0.999692,0.0000410918,0.00001616216,0.00003165296,0.0002001324,0.00001907254],"domain_scores_gemma":[0.9998147,0.00003637307,0.00002682713,0.00002725339,0.00008493176,0.000009956501],"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":[0.0004196308,0.00007427636,0.0006973052,0.0004816444,0.00007885539,0.0003727734,0.0002719495,0.01677568,0.3327371,0.01836194,0.003603772,0.626125],"study_design_scores_gemma":[0.0001536795,0.0006790184,0.002334481,0.0001384383,0.0001676659,0.003032014,0.000123912,0.5850478,0.352003,0.004637603,0.0515507,0.0001318056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02069064,0.002622177,0.973989,0.0001398129,0.0001830618,0.00006904151,0.00004602421,0.0004139873,0.001846333],"genre_scores_gemma":[0.240819,0.004280633,0.7411407,0.0001218985,0.000169057,0.0001127162,0.0001989639,0.00008722498,0.01306985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001463863,"threshold_uncertainty_score":0.003356576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04095034264973683,"score_gpt":0.300896298493098,"score_spread":0.2599459558433612,"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."}}