{"id":"W1966509400","doi":"10.1016/j.imavis.2003.09.011","title":"A hybrid quantization scheme for image compression","year":2003,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Government of Alberta","keywords":"Quantization (signal processing); Vector quantization; Linde–Buzo–Gray algorithm; Computer science; Smoothness; Algorithm; Trellis quantization; Image compression; Scheme (mathematics); Mathematics; Artificial intelligence; Image (mathematics); Image processing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004762387,0.0001853049,0.0002136971,0.0001265195,0.0004208778,0.0003475589,0.0004379053,0.00004303107,0.000007258297],"category_scores_gemma":[0.0002755813,0.0001661387,0.00005523401,0.0001772201,0.00005629453,0.001224869,0.000447517,0.0001296242,0.000009077638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001670254,"about_ca_system_score_gemma":0.00002483428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002588336,"about_ca_topic_score_gemma":1.111442e-7,"domain_scores_codex":[0.9984845,0.0001036662,0.0003309401,0.0005706744,0.000207303,0.0003029182],"domain_scores_gemma":[0.9986951,0.0002997324,0.0001832842,0.0005219619,0.0001948472,0.0001050175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002742987,0.000220749,0.0002043983,0.0001739966,0.00001383437,0.0000355758,0.0002517976,0.00004918853,0.4848367,0.08502638,0.02652519,0.4026347],"study_design_scores_gemma":[0.0008078815,0.0001455182,0.0001871026,0.0002560661,0.00000427293,0.00006677578,0.00001907661,0.7047395,0.2490322,0.01660584,0.0277751,0.0003605991],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008666429,0.0002138738,0.9894587,0.0001678046,0.0001843159,0.0003408097,0.000006273189,0.0004743403,0.0004874762],"genre_scores_gemma":[0.2214784,0.00002383621,0.7781946,0.0002019427,0.00003708161,0.000007367573,0.00001358534,0.00001522596,0.00002798205],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7046903,"threshold_uncertainty_score":0.6774941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646850848751606,"score_gpt":0.3483436278504544,"score_spread":0.3318751193629383,"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."}}