{"id":"W3013680898","doi":"10.5539/mas.v14n4p52","title":"An Efficient Two-Level Dictionary-Based Technique for Segmentation and Compression Compound Images","year":2020,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gamut; Computer science; Lossless compression; Artificial intelligence; Block (permutation group theory); Pattern recognition (psychology); Set (abstract data type); Pixel; Image compression; Segmentation; Image (mathematics); Representation (politics); Encoder; Color space; Computer vision; Data compression; Algorithm; Image processing; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004229835,0.0001568348,0.0001415096,0.0001004153,0.0007288124,0.0003684714,0.0009634335,0.00003458429,0.000002476467],"category_scores_gemma":[0.0000088427,0.0001348823,0.00002220823,0.0003987006,0.0002727952,0.0005432282,0.0003356227,0.00009839362,0.000003305406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004368098,"about_ca_system_score_gemma":0.0001202979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009197091,"about_ca_topic_score_gemma":4.226089e-7,"domain_scores_codex":[0.9981661,0.0000228672,0.0001934836,0.0008158841,0.000517488,0.000284132],"domain_scores_gemma":[0.9990641,0.00006789378,0.0001010215,0.0004157607,0.00009771359,0.000253502],"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":[0.00002469047,0.0000901099,0.00002436343,0.00001490067,9.344477e-7,0.000001087942,0.0003232065,0.01660132,0.9607919,0.002918328,0.00007404188,0.01913505],"study_design_scores_gemma":[0.0004962237,0.00009790938,0.000388815,0.00001107819,0.000002466299,0.000002765793,0.00002078244,0.7960644,0.2012352,0.001459252,0.0000749224,0.0001462576],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005435016,0.00002599123,0.9928738,0.0004435973,0.00007744938,0.000753623,0.00004746479,0.0001864341,0.0001565825],"genre_scores_gemma":[0.5954418,7.831642e-7,0.4040079,0.0003993217,0.00002774639,0.00009902907,0.00001613133,0.000005641722,0.000001549432],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7794631,"threshold_uncertainty_score":0.5605509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03311945467718457,"score_gpt":0.2903346016448842,"score_spread":0.2572151469676996,"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."}}