{"id":"W2094295862","doi":"10.1007/s11554-011-0208-7","title":"Subsample-based image compression for capsule endoscopy","year":2011,"lang":"en","type":"article","venue":"Journal of Real-Time Image Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Computer science; Artificial intelligence; Computer vision; Chrominance; Lossless compression; Image compression; Data compression; Clipping (morphology); Pixel; Image processing; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001849959,0.000433411,0.000274623,0.0004762757,0.0001507493,0.0003185166,0.0002577593,0.0004186799,0.002146271],"category_scores_gemma":[0.0007548209,0.0001313843,0.0002221932,0.0005104698,0.0001438395,0.0003998229,0.0002410411,0.0003211541,0.0005423586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000122464,"about_ca_system_score_gemma":0.0001740093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007078346,"about_ca_topic_score_gemma":0.001150523,"domain_scores_codex":[0.9998586,0.00002867805,0.000008899071,0.00001424595,0.00008015911,0.000009301835],"domain_scores_gemma":[0.9997756,0.0000936642,0.00002151154,0.00004052411,0.00006001774,0.000008681664],"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":[0.001193272,0.0001141012,0.0006754238,0.000210678,0.0000697202,0.0002628693,0.00008066963,0.01522757,0.3045889,0.003562331,0.004062404,0.669952],"study_design_scores_gemma":[0.00007403977,0.0005991714,0.005156083,0.000050289,0.0001338725,0.001808337,0.00007492639,0.6472391,0.3289594,0.002304481,0.01355394,0.00004619798],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1466738,0.004025218,0.8433161,0.0005806535,0.0003853698,0.00007834988,0.0002495948,0.0009203636,0.003770465],"genre_scores_gemma":[0.5364082,0.002771476,0.4509489,0.000350102,0.0004102053,0.00008570746,0.0005442536,0.00016791,0.008313335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002146271,"threshold_uncertainty_score":0.007179976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03080377291511891,"score_gpt":0.3076789209883232,"score_spread":0.2768751480732042,"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."}}