{"id":"W2919080262","doi":"10.1109/tpami.2019.2903062","title":"Learning Raw Image Reconstruction-Aware Deep Image Compressors","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Artificial intelligence; Computer science; JPEG; Computer vision; RGB color model; Image compression; Iterative reconstruction; Deep learning; Data compression; 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":[0.0007725945,0.001126106,0.000578889,0.0003262587,0.0001615594,0.0005647003,0.001296852,0.0007880394,0.001499044],"category_scores_gemma":[0.002371519,0.0003103814,0.0003174334,0.0003170007,0.0007594049,0.001646067,0.001158047,0.00159437,0.0002762664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000796391,"about_ca_system_score_gemma":0.0007724701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001932789,"about_ca_topic_score_gemma":0.003376678,"domain_scores_codex":[0.9997597,0.00003195048,0.00001007379,0.00004489364,0.0001152682,0.00003808334],"domain_scores_gemma":[0.9994535,0.000190155,0.0001035635,0.00009056491,0.0001234743,0.00003884776],"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":[0.0002229093,0.0001594602,0.00157883,0.000136989,0.00008107271,0.0001197186,0.00006527889,0.7597239,0.03867737,0.01364284,0.002326728,0.1832649],"study_design_scores_gemma":[0.000004146464,0.00003644498,0.00007560366,0.000005359617,0.000006238492,0.00001445287,0.000004027443,0.9913834,0.00651599,0.001680875,0.000270584,0.000002883878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0719074,0.000344529,0.9236146,0.0003527619,0.00004888742,0.00006257049,0.0001159745,0.001173927,0.002379415],"genre_scores_gemma":[0.7788254,0.0004685996,0.2141345,0.0003437638,0.00007734943,0.000131504,0.0003323222,0.0001579202,0.005528595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001932789,"threshold_uncertainty_score":0.005778253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01377748640631952,"score_gpt":0.2717217225267818,"score_spread":0.2579442361204623,"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."}}