{"id":"W2170183661","doi":"10.1109/isimp.2001.925359","title":"Noncausal predictive lattice model for image compression","year":2002,"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 Windsor","funders":"","keywords":"Lossy compression; Lossless compression; Image compression; Computer science; Data compression; Redundancy (engineering); Algorithm; Artificial intelligence; Codec; Predictive coding; Image plane; Image (mathematics); Computer vision; Mathematics; Image processing; Coding (social sciences)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003942506,0.000362191,0.0004106844,0.0002753051,0.0002218936,0.0006091633,0.000728782,0.000553967,0.001830322],"category_scores_gemma":[0.001209586,0.0001767236,0.0003109776,0.0003609858,0.000623733,0.00106771,0.0003458963,0.001005845,0.0003349929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005246298,"about_ca_system_score_gemma":0.0006279973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001583367,"about_ca_topic_score_gemma":0.001431819,"domain_scores_codex":[0.9997465,0.00006371972,0.000008133737,0.00003105173,0.0001317114,0.00001890815],"domain_scores_gemma":[0.9996737,0.0001666921,0.00004305237,0.0000406904,0.00005951281,0.00001638719],"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":[0.0001009584,0.00005609181,0.0003428598,0.0001085728,0.00002804985,0.0001705179,0.0000678711,0.6522509,0.008965981,0.2918029,0.001774364,0.04433082],"study_design_scores_gemma":[0.000005533811,0.00001356224,0.00002735505,0.000002498803,0.000002570788,0.00002170067,0.000003474386,0.9845927,0.0006426289,0.01406484,0.0006188063,0.000004354284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01076443,0.0003982033,0.984659,0.000299669,0.00006855682,0.00002082709,0.00006842377,0.0001847211,0.003536133],"genre_scores_gemma":[0.8344932,0.001306613,0.154677,0.0002493791,0.0001264731,0.0001701392,0.0002128065,0.00006139508,0.008703055],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001830322,"threshold_uncertainty_score":0.006123006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0426688238464089,"score_gpt":0.3016464091005967,"score_spread":0.2589775852541878,"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."}}