{"id":"W2081258946","doi":"10.1109/iscas.2012.6271419","title":"A low-power subsample-based image compression algorithm for capsule endoscopy","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Discrete cosine transform; Quantization (signal processing); Algorithm; Compression ratio; Computer science; Image compression; Data compression; RGB color model; Artificial intelligence; Computer vision; Image processing; Image (mathematics); Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0003029158,0.0002373263,0.0002407699,0.0001313787,0.0001851529,0.0001158027,0.001192981,0.00008665265,0.0001734633],"category_scores_gemma":[0.00005896896,0.0001907238,0.0001034216,0.0002333362,0.00006226738,0.001784105,0.0004613076,0.0001351494,0.00006839388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005095235,"about_ca_system_score_gemma":0.00004822405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001609904,"about_ca_topic_score_gemma":5.73746e-7,"domain_scores_codex":[0.9982321,0.0000657696,0.0002948402,0.0004487517,0.0003195232,0.0006389839],"domain_scores_gemma":[0.9980843,0.0002337708,0.0001300859,0.001147657,0.0001458799,0.0002583228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000321424,0.0011737,0.000139171,0.00006847784,0.00002306322,0.000008293794,0.0002963518,0.00001369381,0.5807712,0.03462408,0.1053991,0.2774507],"study_design_scores_gemma":[0.000665316,0.00008557257,0.00009804389,0.00004380168,0.00000327453,0.000004102895,0.000008485946,0.06354862,0.8953815,0.001676818,0.03820143,0.0002830292],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003368825,0.0001121084,0.9963297,0.0001887635,0.0004533954,0.0005841298,0.00006868529,0.001121395,0.0008049174],"genre_scores_gemma":[0.03211183,0.00000405786,0.9665833,0.0007285438,0.00008320923,0.0002256559,0.00004599636,0.00002581853,0.0001916648],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3146103,"threshold_uncertainty_score":0.7777489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779221257141551,"score_gpt":0.3043838904480465,"score_spread":0.286591677876631,"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."}}