{"id":"W1518500973","doi":"10.1109/83.841931","title":"L/sub ∞/ constrained high-fidelity image compression via adaptive context modeling","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Lossless compression; Image compression; Computer science; Fidelity; Data compression; Algorithm; Context (archaeology); Image (mathematics); Wavelet; High fidelity; Data compression ratio; Compression (physics); Artificial intelligence; Pattern recognition (psychology); Image processing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002760245,0.0004395869,0.0004290603,0.0002405772,0.0008552934,0.0004005815,0.001044033,0.0001539324,0.0002079944],"category_scores_gemma":[0.000009087558,0.0004216347,0.0001398825,0.0005656409,0.0002651352,0.003920838,0.00001486105,0.000710051,0.0001269468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001318954,"about_ca_system_score_gemma":0.0001483196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008710871,"about_ca_topic_score_gemma":0.000009497051,"domain_scores_codex":[0.9970672,0.0001641922,0.0006618171,0.0009984978,0.0005469041,0.0005613948],"domain_scores_gemma":[0.998186,0.0001240284,0.0001841577,0.000911076,0.0003655888,0.000229164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009987961,0.0002242012,1.284709e-7,0.00003367493,0.00001267866,0.00002445966,0.0002481107,0.01144654,0.1312904,0.0000653657,0.00008485519,0.8564697],"study_design_scores_gemma":[0.0005285468,0.00007912168,0.000001990501,0.0002922507,0.00001493066,0.00003705641,0.00004596064,0.6705193,0.3241581,0.00389237,0.00006726658,0.0003631353],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003897871,0.0001408199,0.9927343,0.000277489,0.0001775227,0.000418214,0.00005727874,0.001580847,0.000715633],"genre_scores_gemma":[0.6322045,0.00004141882,0.3672647,0.0002996541,0.00002692036,0.00006766712,0.000004672826,0.00003009971,0.00006035691],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8561066,"threshold_uncertainty_score":0.9998236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01948508971450302,"score_gpt":0.2730916628622603,"score_spread":0.2536065731477573,"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."}}