{"id":"W2154843032","doi":"10.1109/icip.2009.5413399","title":"An efficient low random-access delay panorama-based multiview video coding scheme","year":2009,"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":"University of British Columbia","funders":"","keywords":"Computer science; Random access; Coding (social sciences); Coding tree unit; Panorama; Multiview Video Coding; Computer vision; Context-adaptive binary arithmetic coding; Algorithmic efficiency; Artificial intelligence; Data compression; Decoding methods; Algorithm; Video processing; Mathematics; Computer network; Video tracking; Statistics","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":[0.0004365948,0.0002436405,0.0003128211,0.0002218995,0.0002878248,0.0006974646,0.002532992,0.0001174692,0.00003715977],"category_scores_gemma":[0.0001263829,0.0001853858,0.000116194,0.0006725938,0.00005748448,0.0004987355,0.0002434185,0.0002154351,0.00005554757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004150867,"about_ca_system_score_gemma":0.00007132031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001536941,"about_ca_topic_score_gemma":0.00000214827,"domain_scores_codex":[0.9980665,0.0001035291,0.0003370521,0.0006458513,0.0003947122,0.0004523072],"domain_scores_gemma":[0.998268,0.0001558185,0.0001253347,0.001190869,0.0001199514,0.0001400049],"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":[0.0001354671,0.001383881,0.000827142,0.00007465504,0.00002753842,0.0001232974,0.0002637162,0.06269637,0.0318605,0.04141791,0.004158647,0.8570309],"study_design_scores_gemma":[0.001479811,0.0001593299,0.0005813541,0.0001460544,0.000004050627,0.00000552238,0.00001560783,0.9117243,0.08412468,0.0005704296,0.0008644455,0.000324371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1227754,0.000221469,0.8715473,0.001810411,0.000199516,0.0002317968,7.03889e-7,0.002258105,0.0009552427],"genre_scores_gemma":[0.9374874,0.00001728841,0.06098488,0.00137814,0.00002366812,0.00002349214,0.000001618123,0.000007563453,0.00007591076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8567065,"threshold_uncertainty_score":0.7559814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02561293439432004,"score_gpt":0.3015436848867912,"score_spread":0.2759307504924711,"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."}}