{"id":"W2167667873","doi":"10.1145/2700300","title":"Decoder-Complexity-Aware Encoding of Motion Compensation for Multiple Heterogeneous Receivers","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University; British Columbia Innovation Council","keywords":"Computer science; Encoder; Codec; Encoding (memory); Computational complexity theory; Motion compensation; Interpolation (computer graphics); Real-time computing; Motion vector; Decoding methods; Focus (optics); Computer engineering; Algorithm; Computer hardware; Motion (physics); Computer vision; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004290135,0.0006917966,0.0004845716,0.0003797837,0.0002772892,0.0005054296,0.0005001938,0.0004791714,0.0009291618],"category_scores_gemma":[0.001716782,0.0002387677,0.0004059993,0.0003543458,0.0002531511,0.0006859174,0.0005434422,0.0006331474,0.0002939283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000610815,"about_ca_system_score_gemma":0.0009208118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001817993,"about_ca_topic_score_gemma":0.004777377,"domain_scores_codex":[0.999592,0.00007192679,0.0000232283,0.00004880736,0.0002308948,0.00003305395],"domain_scores_gemma":[0.9995387,0.000198024,0.0000536295,0.00006861582,0.0001236715,0.00001739248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002754125,0.0001381028,0.001322142,0.0001317573,0.0000752274,0.0002616742,0.000180192,0.4568495,0.2072494,0.01596945,0.001752223,0.3157949],"study_design_scores_gemma":[0.00001004931,0.00004776248,0.000202336,0.00000500062,0.00001445303,0.00009721131,0.0000115942,0.9702877,0.02687694,0.001538072,0.0009002052,0.00000864389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01716283,0.0001442149,0.9813928,0.00005661947,0.00001542962,0.00002908176,0.00001668525,0.0001910607,0.000991325],"genre_scores_gemma":[0.3683135,0.0002955273,0.6278402,0.00007789563,0.0000514788,0.00005838082,0.0001147567,0.0001003964,0.003147966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001817993,"threshold_uncertainty_score":0.004431784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1161947432051427,"score_gpt":0.3220635813067495,"score_spread":0.2058688381016068,"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."}}