{"id":"W2169119453","doi":"10.1109/83.841529","title":"Indexing the output points of an LBVQ used for image transform coding","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Search engine indexing; JPEG; Discrete cosine transform; Transform coding; Mathematics; Data compression; Lattice (music); Image quality; Computer science; Algorithm; Pattern recognition (psychology); Image processing; Coding (social sciences); Artificial intelligence; Image (mathematics); 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":[0.0004290529,0.0002250957,0.000241797,0.0001729712,0.0006301256,0.0002781446,0.001067926,0.00007494491,0.00004800334],"category_scores_gemma":[0.00000911029,0.0001775666,0.0001217963,0.0004512399,0.0001612099,0.003407701,0.000003550353,0.0003010522,0.000006503071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005353612,"about_ca_system_score_gemma":0.00009937278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000111242,"about_ca_topic_score_gemma":0.000008985589,"domain_scores_codex":[0.9983101,0.00006201091,0.0004300385,0.0004756144,0.0003377523,0.0003844907],"domain_scores_gemma":[0.99879,0.0001679922,0.000138353,0.0006535175,0.0001552356,0.0000949266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005285041,0.0001743484,3.247875e-7,0.00009900416,0.000009818226,0.000003434801,0.001488482,0.0004754371,0.04462008,0.00007698607,0.00004240676,0.9529569],"study_design_scores_gemma":[0.0006060912,0.0001407848,0.000007300891,0.0002312147,0.0000208251,0.00002719761,0.0001094784,0.1684118,0.8251506,0.004193934,0.0008448267,0.0002559707],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002771253,0.00003026564,0.9949921,0.000615558,0.00008911388,0.0005130388,0.00004735462,0.000477971,0.0004633824],"genre_scores_gemma":[0.5642714,0.00001805177,0.4352306,0.0001655798,0.00002314961,0.0001103176,0.000002901604,0.00002834598,0.0001496517],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9527009,"threshold_uncertainty_score":0.7240956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02248534866397376,"score_gpt":0.3052128568076727,"score_spread":0.282727508143699,"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."}}