{"id":"W2119360038","doi":"10.1109/glocom.2010.5683783","title":"Distortion Analysis of Wyner-Ziv Distributed Video Coding","year":2010,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Codec; Encoder; Coding (social sciences); Decoding methods; Coding tree unit; Bottleneck; ENCODE; Multiview Video Coding; Algorithm; Video quality; Real-time computing; Artificial intelligence; Video processing; Video tracking; Computer hardware; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001297287,0.00006938408,0.000169971,0.0001947336,0.00002481843,0.00001277704,0.0002099287,0.0000697119,0.0002335535],"category_scores_gemma":[0.00004017794,0.00007060374,0.00008514489,0.0006423153,0.00003301332,0.00009606477,0.00003741758,0.0001577104,0.000004380539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002672334,"about_ca_system_score_gemma":0.000003576805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000645988,"about_ca_topic_score_gemma":0.0004580235,"domain_scores_codex":[0.9995039,0.00001181114,0.0002261192,0.00007242342,0.00009976298,0.00008604164],"domain_scores_gemma":[0.9992984,0.00005771887,0.00003957285,0.0005109495,0.00005888419,0.00003447379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007781265,0.0001380547,0.03479094,0.00008829064,0.001244685,0.000001161107,0.0007233593,0.007819288,0.8610752,0.07290515,0.006322591,0.01488355],"study_design_scores_gemma":[0.0001554014,0.00001311421,0.09077594,0.00001520597,0.0004524429,9.190907e-7,0.00007181492,0.4722488,0.4195439,0.0005038864,0.01586491,0.0003536382],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.662996,0.00002905697,0.330961,0.0000651343,0.00006656007,0.00006560484,0.00004512668,0.0008365106,0.004935006],"genre_scores_gemma":[0.9958417,0.00003726784,0.003909777,0.000006880484,0.000008656824,0.00001149492,0.0001556487,0.0000098767,0.00001866363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4644295,"threshold_uncertainty_score":0.2879137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008511844442523826,"score_gpt":0.2391644776861722,"score_spread":0.2306526332436484,"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."}}