{"id":"W2007294857","doi":"10.1117/12.862846","title":"Variable length coding for binary sources and applications in video compression","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Blackberry (Canada)","funders":"University of Waterloo","keywords":"Computer science; Decoding methods; Lossless compression; Entropy encoding; Context-adaptive binary arithmetic coding; Data compression; Binary number; Encoding (memory); Tunstall coding; Algorithm; Coding (social sciences); Entropy (arrow of time); Theoretical computer science; Arithmetic; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003514719,0.0003985167,0.0002400278,0.0006547653,0.0002161177,0.0004755854,0.0005903044,0.000611692,0.002219389],"category_scores_gemma":[0.001541812,0.0001412576,0.0001844334,0.0009394716,0.0005609751,0.0008439384,0.0004409428,0.0009775413,0.0008260722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003619344,"about_ca_system_score_gemma":0.0002847255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003116659,"about_ca_topic_score_gemma":0.0004683366,"domain_scores_codex":[0.9997403,0.00006258147,0.00001418926,0.00002592442,0.0001405612,0.00001647883],"domain_scores_gemma":[0.9996228,0.0002120915,0.00004480854,0.00005509858,0.00005606412,0.000009104268],"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":[0.0002106016,0.00006143749,0.0003123535,0.000458368,0.00002374162,0.0001865561,0.000114671,0.03445751,0.09580514,0.373472,0.003977629,0.49092],"study_design_scores_gemma":[0.00009337745,0.0003455721,0.0006426453,0.00038268,0.0000469171,0.001065728,0.00004121883,0.496231,0.1504713,0.2653182,0.08527251,0.00008876587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01192223,0.00580934,0.97276,0.0004328862,0.0001920197,0.00006212696,0.00008364452,0.0004017689,0.008336077],"genre_scores_gemma":[0.2924463,0.008559518,0.6881727,0.0004674823,0.000437094,0.0002097156,0.0003533249,0.0001486891,0.009205106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002219389,"threshold_uncertainty_score":0.007424533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01013968423179019,"score_gpt":0.2457579350305854,"score_spread":0.2356182507987953,"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."}}