{"id":"W2095054567","doi":"10.1109/tip.2014.2383324","title":"An Efficient DCT-Based Image Compression System Based on Laplacian Transparent Composite Model","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; JPEG; Quantization (signal processing); Computational complexity theory; Discrete cosine transform; Image compression; Transform coding; Image quality; Artificial intelligence; Coding (social sciences); Entropy encoding; Algorithm; Data compression; Entropy (arrow of time); Computer vision; Mathematics; Image processing; Image (mathematics); Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004347645,0.0005096305,0.0004507466,0.0005173392,0.0009778946,0.0005637672,0.001409661,0.0001463519,0.000008863059],"category_scores_gemma":[0.000005135721,0.0004641363,0.0001624322,0.0005538433,0.0001523127,0.001134422,0.000006484561,0.0005679337,0.00003402428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002333819,"about_ca_system_score_gemma":0.0001500175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009797104,"about_ca_topic_score_gemma":0.000002791318,"domain_scores_codex":[0.9965183,0.0003170697,0.0005831726,0.00113213,0.0008765257,0.0005728483],"domain_scores_gemma":[0.9974742,0.0001682859,0.0002627599,0.001507858,0.0002494905,0.0003374245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001258973,0.0007375252,8.556909e-7,0.0002350964,0.000003502192,0.000008971471,0.0001213825,0.8062146,0.1389012,0.00007815874,0.00002789114,0.05354495],"study_design_scores_gemma":[0.0006830062,0.0001782596,0.000006676659,0.0007716271,0.00001658263,0.000003777901,0.00001211957,0.6230258,0.3749032,0.00003899857,0.00003303823,0.0003270016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001256599,0.00001608129,0.9947388,0.000248965,0.000234833,0.0004774441,0.00007966286,0.002357505,0.0005900881],"genre_scores_gemma":[0.5894167,6.243297e-7,0.4100668,0.0003421461,0.00001761583,0.00009475671,0.00001231923,0.000040619,0.000008405521],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5881602,"threshold_uncertainty_score":0.999781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740912989096638,"score_gpt":0.2849233426714591,"score_spread":0.2675142127804928,"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."}}