{"id":"W2313128359","doi":"10.1109/embc.2014.6944118","title":"An improved YEF-DCT based compression algorithm for video capsule endoscopy","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Discrete cosine transform; Computer science; Capsule endoscopy; Lossless compression; Data compression; Computer vision; Artificial intelligence; Peak signal-to-noise ratio; Image compression; Algorithm; 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":[0.0002218411,0.0004213554,0.000364593,0.000693315,0.000227824,0.0003950764,0.0004630962,0.0005059475,0.001927718],"category_scores_gemma":[0.0006875193,0.0001357101,0.0002790223,0.0008171222,0.000161116,0.000606438,0.0002178468,0.0004650107,0.0007618407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003305981,"about_ca_system_score_gemma":0.0005288082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002783844,"about_ca_topic_score_gemma":0.002839025,"domain_scores_codex":[0.9997481,0.00002006898,0.00001616384,0.00003385335,0.0001650716,0.00001674404],"domain_scores_gemma":[0.9998332,0.000027636,0.0000126072,0.00001806753,0.0001007091,0.000007701903],"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":[0.0003329116,0.00008944509,0.000649701,0.0001624217,0.00003138183,0.0003435062,0.00005356686,0.02853551,0.1984401,0.006276828,0.004717408,0.7603672],"study_design_scores_gemma":[0.00008396956,0.0003981793,0.002812239,0.00004469625,0.00003822202,0.002314081,0.00003434247,0.8025281,0.1624459,0.001013374,0.02822452,0.00006238184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0347829,0.001800479,0.9574733,0.0002236676,0.0002652752,0.0001270373,0.0001340392,0.0008106158,0.004382701],"genre_scores_gemma":[0.2167206,0.001813203,0.7674019,0.0002059322,0.0001552693,0.0001312169,0.0006863314,0.00009613394,0.01278938],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002783844,"threshold_uncertainty_score":0.006448805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01172414957364703,"score_gpt":0.295217305651744,"score_spread":0.2834931560780969,"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."}}