{"id":"W1967320979","doi":"10.1117/12.632676","title":"A VLSI architecture for high performance CABAC encoding","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Context-adaptive binary arithmetic coding; Computer science; Context-adaptive variable-length coding; Entropy encoding; Huffman coding; Encoder; Arithmetic coding; Parallel computing; Application-specific integrated circuit; Encoding (memory); Binary number; Adaptive coding; Very-large-scale integration; Computer hardware; Computer architecture; Data compression; Arithmetic; Algorithm; Lossless compression; Embedded system; 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":[0.0001288231,0.0003504882,0.000164182,0.000498988,0.0003244507,0.0004773522,0.000838463,0.0004079404,0.00758788],"category_scores_gemma":[0.000343621,0.0001599246,0.0001805177,0.0005105075,0.000143165,0.0005383108,0.0001958384,0.0003894434,0.002017245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000625111,"about_ca_system_score_gemma":0.0007356033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002060653,"about_ca_topic_score_gemma":0.003321202,"domain_scores_codex":[0.9998589,0.00001647057,0.000007263011,0.00002380205,0.00007305369,0.00002054959],"domain_scores_gemma":[0.9998201,0.00002220751,0.00001408545,0.00002259139,0.0001112089,0.000009856759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002656144,0.0001043447,0.0009111632,0.000475142,0.00005419421,0.0003686136,0.0001204695,0.02195076,0.3864915,0.02797451,0.02502866,0.5362551],"study_design_scores_gemma":[0.0002094653,0.002107953,0.00288239,0.0001426244,0.0001891372,0.002671202,0.0001144435,0.3941533,0.2987649,0.01263217,0.2860246,0.0001079313],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05510103,0.003047052,0.8852654,0.0007557505,0.0004722245,0.0002873976,0.0005516974,0.01052999,0.04398949],"genre_scores_gemma":[0.4454986,0.001031056,0.530009,0.0004132377,0.0001459503,0.0001561893,0.0009513345,0.0001359973,0.02165868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00758788,"threshold_uncertainty_score":0.02538401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01133659343506039,"score_gpt":0.2215334183319549,"score_spread":0.2101968248968945,"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."}}