{"id":"W4380303272","doi":"10.1109/cisce58541.2023.10142615","title":"Context Similarity-Enabled CU Partitioning Algorithm in VVC","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Quadtree; Coding (social sciences); Computer science; Reference frame; Algorithm; Algorithmic efficiency; Reference software; Data compression; Coding tree unit; Software; Frame (networking); Mathematics; Decoding methods; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003280863,0.00008890679,0.0001302728,0.0002452218,0.0001362406,0.0001486938,0.0007023499,0.00007131037,0.00003279185],"category_scores_gemma":[0.0001051801,0.00007663549,0.00003583661,0.001057495,0.00002979962,0.0003110458,0.0004856112,0.0001888607,0.0003181674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002213393,"about_ca_system_score_gemma":0.00002778474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009665504,"about_ca_topic_score_gemma":0.00004124416,"domain_scores_codex":[0.999001,0.00004513016,0.0001824602,0.0002960215,0.0001726075,0.0003027263],"domain_scores_gemma":[0.99935,0.0001246894,0.00003360165,0.000419005,0.00003518814,0.00003752198],"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":[0.000001508818,0.0000455755,0.002490189,0.000008429964,0.000007214696,0.000101108,0.0004057894,0.0003238837,0.0004345564,0.09707767,0.03366945,0.8654346],"study_design_scores_gemma":[0.0006411027,0.00006863911,0.00647636,0.0001050483,0.000001731925,0.000009228533,0.0008777254,0.8466969,0.01760012,0.1081194,0.01906031,0.0003434269],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03781028,0.0001670436,0.93137,0.01209297,0.0006114766,0.0001921686,0.000001775269,0.006626147,0.01112811],"genre_scores_gemma":[0.9712014,0.00004807471,0.02563922,0.0005546745,0.00001902264,0.00004657613,0.000001905971,0.00000576948,0.002483348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9333911,"threshold_uncertainty_score":0.4089504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03353970483577227,"score_gpt":0.2671701139063831,"score_spread":0.2336304090706108,"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."}}