{"id":"W2041148577","doi":"10.1109/vcip.2013.6706447","title":"Transform coefficient coding design for AVS2 video coding standard","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Computer science; Context-adaptive binary arithmetic coding; Coding tree unit; Coding (social sciences); Context-adaptive variable-length coding; Entropy encoding; Encoder; Variable-length code; Algorithmic efficiency; Algorithm; Data compression; Decoding methods; Mathematics; Statistics","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":[0.0003368061,0.0003946078,0.0002091982,0.0005603656,0.0002960257,0.0005395154,0.0004929308,0.0004025441,0.002183076],"category_scores_gemma":[0.00106493,0.0001292942,0.0001943773,0.0005877556,0.0001968176,0.0004340294,0.0003093545,0.0005673784,0.0007736794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000667588,"about_ca_system_score_gemma":0.0007958642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002707968,"about_ca_topic_score_gemma":0.003307537,"domain_scores_codex":[0.9994808,0.00007564576,0.00003108365,0.00005849924,0.0003124299,0.00004161761],"domain_scores_gemma":[0.9995008,0.00004125973,0.00003028814,0.00003847095,0.0003745295,0.0000147953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004441733,0.0000573656,0.001094526,0.0002119955,0.00003664433,0.0003781929,0.0002370646,0.04448776,0.4375005,0.05058976,0.0138559,0.451106],"study_design_scores_gemma":[0.0001286196,0.0005319078,0.001497419,0.00006453125,0.00004497135,0.000915477,0.0001186626,0.6594765,0.2653986,0.007861404,0.06389342,0.00006853756],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01847883,0.0003141837,0.9732726,0.0002240114,0.0001297513,0.0001946809,0.0001754286,0.0004783899,0.00673212],"genre_scores_gemma":[0.4486192,0.0005777934,0.5391638,0.000252851,0.0001152354,0.0003867832,0.0009329289,0.0001412998,0.009810088],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002707968,"threshold_uncertainty_score":0.007303119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0403951584665348,"score_gpt":0.2646679741396889,"score_spread":0.2242728156731541,"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."}}