{"id":"W4382394974","doi":"10.18280/ts.400332","title":"Deep Learning Based Compression with Classification Model on CMOS Image Sensors","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"CMOS; Computer science; Artificial intelligence; Image compression; Compression (physics); Computer vision; Deep learning; Image (mathematics); Data compression; Pattern recognition (psychology); Electronic engineering; Engineering; Image processing; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001198095,0.0001766403,0.000129221,0.0001389332,0.0001184625,0.0000517991,0.00008960844,0.00004009502,0.0001151934],"category_scores_gemma":[0.000005745777,0.000154587,0.00004345494,0.00023303,0.00003193653,0.00009349102,0.000008327297,0.0002088187,0.0001764848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005693715,"about_ca_system_score_gemma":0.000009361169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002307571,"about_ca_topic_score_gemma":0.000001545656,"domain_scores_codex":[0.9990116,0.00003385894,0.0001699586,0.0002094846,0.0003012616,0.0002738583],"domain_scores_gemma":[0.999665,0.00006100076,0.00003218054,0.0001364249,0.00003243122,0.00007291335],"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":[0.00003771645,0.00002658451,0.0001990581,0.00003768432,0.00001529915,0.00001140887,0.0002846643,0.9177766,0.0773839,0.00006710618,0.0009225704,0.003237339],"study_design_scores_gemma":[0.0006375416,0.00006211592,0.004374901,0.00007124148,0.00001621563,0.0000012687,0.0001955802,0.9864316,0.007362725,0.00001762238,0.0006281719,0.0002010175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8788709,0.00001039241,0.1150982,0.000273473,0.00006681439,0.0002407913,0.000006876611,0.001551562,0.003881013],"genre_scores_gemma":[0.9981164,0.000006840737,0.001395038,0.00006312193,0.00005943863,0.00002730383,0.00007470752,0.00005375224,0.000203412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1192455,"threshold_uncertainty_score":0.6303877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01865920198610464,"score_gpt":0.2233479964484642,"score_spread":0.2046887944623595,"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."}}