{"id":"W2544760598","doi":"10.1109/acssc.2011.6189995","title":"Dithered soft decision quantization for baseline JPEG encoding and its joint optimization with huffman coding and quantization table selection","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Dither; Quantization (signal processing); Huffman coding; Rate–distortion theory; JPEG; Algorithm; JPEG 2000; Computer science; Lossless JPEG; Trellis quantization; Coding (social sciences); Mathematics; Data compression; Image compression; Artificial intelligence; Computer vision; Image processing; Statistics; Image (mathematics); Noise shaping","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":[0.0004592747,0.0001677307,0.000176603,0.0002149295,0.0003208795,0.0001475079,0.0001657405,0.00007640688,0.00002034898],"category_scores_gemma":[0.0002294509,0.0001377451,0.00001332981,0.000439615,0.00002183784,0.002084396,0.0001448965,0.00006493679,7.582371e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003004368,"about_ca_system_score_gemma":0.00002824027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001952843,"about_ca_topic_score_gemma":0.00002298015,"domain_scores_codex":[0.9987422,0.00005426821,0.0003037159,0.0005151639,0.0001878406,0.0001967619],"domain_scores_gemma":[0.999034,0.0001438835,0.0002111127,0.0002142159,0.0003195399,0.00007728539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007550234,0.0005088915,0.006134251,0.0004234887,0.0000759285,0.000006537378,0.002480606,0.09010707,0.148897,0.5007368,0.00205013,0.2478243],"study_design_scores_gemma":[0.0004246608,0.0002137281,0.0002289476,0.0001240258,0.000009675137,0.00001667259,0.0000293557,0.9027913,0.09380656,0.002048486,0.0001166389,0.0001898938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002860109,0.00007549489,0.9958005,0.00005056163,0.0000659102,0.0005734512,0.000005054706,0.0004529009,0.0001159964],"genre_scores_gemma":[0.3535674,0.0001514185,0.6460927,0.00005009854,0.00001775822,0.00003525241,0.00002605445,0.00001570473,0.00004369591],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8126843,"threshold_uncertainty_score":0.5617083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04943102723055369,"score_gpt":0.2703424789480122,"score_spread":0.2209114517174585,"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."}}