{"id":"W2018284587","doi":"10.1109/vcip.2011.6116045","title":"Multiple description video coding against both erasure and bit errors by compressive sensing","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Erasure; Encoder; Lossy compression; Network packet; Multiview Video Coding; Compressed sensing; Coding (social sciences); Real-time computing; Fountain code; Video tracking; Video processing; Computer hardware; Decoding methods; Computer network; Algorithm; Artificial intelligence; Block code; Concatenated error correction code","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":[0.0007207188,0.0004396373,0.0003975389,0.0004356534,0.0002224338,0.0004237363,0.0008478562,0.0007025757,0.0006342626],"category_scores_gemma":[0.002350771,0.0001641294,0.0002607427,0.000485037,0.0005249447,0.001145241,0.0007787454,0.000775793,0.0002442929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002449648,"about_ca_system_score_gemma":0.0004989816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001031653,"about_ca_topic_score_gemma":0.001200773,"domain_scores_codex":[0.9994302,0.0001107869,0.00002430589,0.00005231365,0.0003353623,0.00004694735],"domain_scores_gemma":[0.9991782,0.0003131152,0.0001590718,0.0001453517,0.0001735133,0.00003072528],"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":[0.0003017388,0.00009783056,0.000782177,0.0002524442,0.0000827602,0.0004044587,0.0002009985,0.3397878,0.1640531,0.09219585,0.003797974,0.3980429],"study_design_scores_gemma":[0.00004351887,0.000206149,0.0002315913,0.00002567728,0.00002523235,0.0002839167,0.00001928006,0.9444134,0.04035211,0.01016439,0.004202363,0.0000324297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01687224,0.0004201174,0.9813809,0.0002157606,0.00005029611,0.00002420199,0.00003423877,0.0001313999,0.0008708754],"genre_scores_gemma":[0.5666602,0.0009641002,0.4279674,0.0002903811,0.0002011245,0.0001150106,0.0002366054,0.00005245885,0.003512659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001031653,"threshold_uncertainty_score":0.003811598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03966990330032994,"score_gpt":0.2450772977584887,"score_spread":0.2054073944581588,"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."}}