{"id":"W2160560076","doi":"10.1109/icip.2009.5414225","title":"Logo insertion transcoding for H.264/AVC compressed video","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Transcoding; Computer science; Scalable Video Coding; Bit rate; Coding (social sciences); Real-time computing; Scheme (mathematics); Block (permutation group theory); Video quality; Computer hardware; Computer vision; Motion compensation; Computer network; 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":[0.0001941348,0.0004755332,0.0002898064,0.0005837824,0.0002399221,0.0002620339,0.0005246483,0.0003267257,0.001671442],"category_scores_gemma":[0.0008505349,0.0001501946,0.0003696148,0.0003145048,0.0002245728,0.0004862976,0.0002789034,0.0004691457,0.0005199565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001994676,"about_ca_system_score_gemma":0.0002746225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001211467,"about_ca_topic_score_gemma":0.002923072,"domain_scores_codex":[0.9998276,0.00002192993,0.00001070895,0.00002283617,0.0001022075,0.00001459088],"domain_scores_gemma":[0.9996635,0.00007678658,0.00003898494,0.00005329991,0.0001551071,0.00001227366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003075697,0.0001116047,0.0008051646,0.0001468875,0.00002641423,0.0007177664,0.0001109484,0.006321391,0.6067871,0.002235454,0.003469301,0.3789605],"study_design_scores_gemma":[0.00006302921,0.0005600358,0.004280735,0.00004609192,0.0001113437,0.003060755,0.0001159138,0.3536933,0.6208833,0.00187254,0.01523897,0.00007392329],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1051235,0.0008651722,0.885362,0.0002867566,0.0002225515,0.0001927765,0.0001584892,0.003032383,0.00475631],"genre_scores_gemma":[0.4887146,0.00107279,0.4991759,0.0002263843,0.0001985845,0.0001073344,0.0005368443,0.0002078113,0.009759775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001671442,"threshold_uncertainty_score":0.005591512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02968614433951357,"score_gpt":0.2691324644105653,"score_spread":0.2394463200710517,"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."}}