{"id":"W2547116575","doi":"10.1109/ccece.2016.7726648","title":"Codebook design for vector quantization using hexagonal partitioning","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Codebook; Vector quantization; Linde–Buzo–Gray algorithm; Computer science; Image compression; Algorithm; Artificial intelligence; Hexagonal crystal system; Partition (number theory); Quantization (signal processing); Image quality; Pattern recognition (psychology); Image (mathematics); Computer vision; Mathematics; Image processing; Combinatorics","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":[0.0001768883,0.00007518673,0.0000775862,0.00005310329,0.0001243633,0.00005754707,0.0003432642,0.00003046957,0.000033641],"category_scores_gemma":[0.00009277761,0.00004992439,0.00002595111,0.00008561846,0.00002326261,0.0009188405,0.0001169991,0.00001864628,0.0000118584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004127556,"about_ca_system_score_gemma":0.00004702161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002356313,"about_ca_topic_score_gemma":6.820721e-7,"domain_scores_codex":[0.9992735,0.00004095971,0.0001498139,0.0002502523,0.0001185644,0.0001668956],"domain_scores_gemma":[0.9992187,0.0002709976,0.00006890314,0.0003038668,0.00009306009,0.00004447279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001673741,0.00002953182,0.00007093443,0.00000690244,0.000006163627,0.000001121203,0.00003307331,0.0004669242,0.3130301,0.6373674,0.005628606,0.04334252],"study_design_scores_gemma":[0.0002822028,0.00007058297,0.00007759206,0.00008575834,0.000002580857,0.000006390266,0.000001815687,0.3050199,0.6280007,0.06011748,0.006156929,0.0001780086],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001597383,0.00001416021,0.998552,0.0003637573,0.0001039282,0.0002454704,0.000005331227,0.0004591732,0.00009646441],"genre_scores_gemma":[0.1237721,0.000003578046,0.8757662,0.000151669,0.00003524693,0.00004309236,0.000001988727,0.000007489226,0.0002186368],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5772499,"threshold_uncertainty_score":0.2035857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1049021048609871,"score_gpt":0.3331233892624537,"score_spread":0.2282212844014666,"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."}}