{"id":"W2118036554","doi":"10.1109/icassp.2008.4517788","title":"Fast block size prediction for MPEG-2 to H.264/AVC transcoding","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Transcoding; Computer science; Discrete cosine transform; Motion estimation; Block (permutation group theory); Computational complexity theory; Data compression; Block size; MPEG-4; Motion vector; MPEG-2; Bit rate; Real-time computing; Algorithm; Rate–distortion optimization; Quarter-pixel motion; Reduction (mathematics); Computer vision; Block-matching algorithm; Image (mathematics); Coding (social sciences); Video tracking; Mathematics; Video processing; Computer network","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":[0.0003262084,0.0003988907,0.0003193783,0.0003928997,0.0002293284,0.0002771008,0.00043068,0.000299959,0.001602571],"category_scores_gemma":[0.001238332,0.0001790543,0.0001821753,0.0003369104,0.0001210451,0.0003451512,0.0002385704,0.0004084362,0.0008200441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003144206,"about_ca_system_score_gemma":0.0005564826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005204776,"about_ca_topic_score_gemma":0.007803441,"domain_scores_codex":[0.9997746,0.00003710199,0.00001468963,0.00003096262,0.0001247672,0.00001784703],"domain_scores_gemma":[0.9995893,0.00009909555,0.00002912029,0.00006714451,0.0001995609,0.0000158734],"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":[0.0005718761,0.00009860156,0.001003918,0.0001234161,0.00002413999,0.000303425,0.0000660162,0.05420788,0.3394425,0.004568742,0.008543681,0.5910458],"study_design_scores_gemma":[0.00004076335,0.0001626742,0.00253216,0.00002863465,0.00001926211,0.0002783444,0.00002651924,0.8756793,0.1129562,0.002033264,0.006210407,0.00003243314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0484741,0.001604742,0.9438242,0.0001728783,0.0001818013,0.0001098816,0.000306764,0.00272768,0.002598079],"genre_scores_gemma":[0.4224133,0.001179855,0.5713955,0.0001027328,0.0001019382,0.0001179812,0.0008839397,0.0001951409,0.003609619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005204776,"threshold_uncertainty_score":0.01034892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05336519898888194,"score_gpt":0.2771318406706462,"score_spread":0.2237666416817643,"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."}}