{"id":"W4294219447","doi":"10.3389/frsip.2022.932873","title":"Joint image compression and denoising via latent-space scalability","year":2022,"lang":"en","type":"article","venue":"Frontiers in Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Image compression; Artificial intelligence; Codec; Noise reduction; Computer vision; Noise (video); Pattern recognition (psychology); Image processing; Image (mathematics); Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001733515,0.0002078629,0.0003397377,0.0002865093,0.0008950392,0.0003821199,0.0005728424,0.0000540222,0.00001975909],"category_scores_gemma":[0.00004003792,0.0002095306,0.00005867112,0.0007311798,0.0001513178,0.001040913,0.0008196149,0.0005398733,0.000001459981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001997991,"about_ca_system_score_gemma":0.00008973524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003576856,"about_ca_topic_score_gemma":0.000001096118,"domain_scores_codex":[0.9974251,0.0005821958,0.0003788441,0.0006567021,0.0005093807,0.0004478183],"domain_scores_gemma":[0.9992836,0.00006206868,0.00016961,0.0002945947,0.00007443642,0.0001157588],"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":[0.0001337508,0.0002269979,0.01011268,0.0002145885,0.00001424078,0.0002192992,0.003863061,0.002307484,0.1224315,0.0001206791,0.001775846,0.8585799],"study_design_scores_gemma":[0.001666015,0.0001754324,0.008024775,0.0001817404,0.00001863607,0.0001549886,0.0004934702,0.9326949,0.02446355,0.03027587,0.001181152,0.0006695021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03133423,0.002868861,0.9642876,0.000481864,0.0003616263,0.000181849,0.000001397431,0.0001234473,0.0003591572],"genre_scores_gemma":[0.6418023,0.000005242972,0.3578782,0.0001765828,0.00003275033,0.00001453007,0.0000016373,0.00001446933,0.00007432572],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9303874,"threshold_uncertainty_score":0.8544409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01630829727655937,"score_gpt":0.253005243806407,"score_spread":0.2366969465298476,"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."}}