{"id":"W2094410090","doi":"10.1117/12.829034","title":"Image compression with Iris-C","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Dynamics (Canada)","funders":"","keywords":"Codec; Computer science; Lossless compression; Data compression; Image compression; Computer vision; Artificial intelligence; Computer hardware; Image processing; Image (mathematics)","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":[0.0002896462,0.0003931299,0.0003053419,0.0008654172,0.0002931682,0.0004938309,0.0005598566,0.0003730635,0.007248846],"category_scores_gemma":[0.001492697,0.0001062684,0.0001897266,0.001144081,0.0003157748,0.0006073973,0.0005654468,0.0006618605,0.001904697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005156386,"about_ca_system_score_gemma":0.0004152504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002209343,"about_ca_topic_score_gemma":0.001522985,"domain_scores_codex":[0.999476,0.00002701581,0.00002729542,0.00006398072,0.0003654141,0.00004030499],"domain_scores_gemma":[0.9993074,0.00009714627,0.00005848005,0.0001555105,0.0003532686,0.00002822148],"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":[0.001353423,0.0001631272,0.00172256,0.0004497741,0.00005391043,0.0007358971,0.0002836714,0.02149999,0.3692863,0.01993624,0.03008552,0.5544296],"study_design_scores_gemma":[0.0001089049,0.0005291004,0.002679921,0.000066263,0.00003841179,0.001932367,0.00008017525,0.2091586,0.7094983,0.001598913,0.07422065,0.00008837969],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2080049,0.002522817,0.6596837,0.0007932099,0.0006035143,0.0006025515,0.002139917,0.03137532,0.09427404],"genre_scores_gemma":[0.6490525,0.001080927,0.3059817,0.0003455407,0.0001345491,0.0001864306,0.002702144,0.0007972802,0.0397189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007248846,"threshold_uncertainty_score":0.02424979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009855603431389167,"score_gpt":0.2455570394827594,"score_spread":0.2357014360513702,"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."}}