{"id":"W4225007044","doi":"10.18280/mmep.090222","title":"Optimized Context-Adaptive Binary Arithmetic Coder in Video Compression Standard Without Probability Estimation","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context-adaptive binary arithmetic coding; Computer science; Data compression; Context-adaptive variable-length coding; Binary number; Arithmetic coding; Arithmetic; Algorithm; Coding (social sciences); Video decoder; Context (archaeology); Decoding methods; Real-time computing; Theoretical computer science; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006779711,0.0001984802,0.0003646184,0.0001821171,0.000194538,0.0001092983,0.0004175669,0.00006612652,0.000007609981],"category_scores_gemma":[0.0000667281,0.000174322,0.0000451863,0.0002931831,0.00004507147,0.0002080227,0.0005192358,0.0004358873,0.000002076858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009159525,"about_ca_system_score_gemma":0.00002531985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000103658,"about_ca_topic_score_gemma":1.502291e-7,"domain_scores_codex":[0.9984633,0.00007045857,0.0003952798,0.000428245,0.000347888,0.0002948096],"domain_scores_gemma":[0.9992241,0.0002090829,0.00007703791,0.0003808987,0.00003703728,0.00007178998],"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":[0.00002250366,0.00007826995,0.00001450278,0.0001384725,0.0000069942,0.000003553505,0.0006761134,0.9623646,0.00008443253,0.03240957,0.0000106701,0.004190371],"study_design_scores_gemma":[0.0004834926,0.0001312135,0.000007976118,0.0002769802,0.000005014782,0.00001216052,0.00004960775,0.8703835,0.0001808342,0.1282312,0.00004966101,0.0001883637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04815716,0.0003491699,0.9500743,0.0003116362,0.00006884363,0.000377592,0.000002713101,0.000596857,0.00006174466],"genre_scores_gemma":[0.6451783,0.00001035186,0.354578,0.00001194028,0.000002764917,0.0001876738,0.000001032589,0.00001066574,0.00001931933],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5970211,"threshold_uncertainty_score":0.7108645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0350393773668619,"score_gpt":0.2298398135262967,"score_spread":0.1948004361594348,"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."}}