{"id":"W4312296627","doi":"10.1007/978-3-031-06947-5_16","title":"On Enhancing Low Bit-Rate Performance of an Image Codec Using Deep Learning-Based Nonlinear Processing","year":2022,"lang":"en","type":"book-chapter","venue":"Signals and communication technology","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Codec; Computer science; JPEG; Artificial intelligence; Convolutional neural network; Noise reduction; Decimation; Image compression; Quantization (signal processing); Coding (social sciences); Image processing; Computer vision; Pattern recognition (psychology); Image (mathematics); Mathematics; Computer hardware","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001146256,0.0002960802,0.000482032,0.0006888316,0.0006276515,0.0001076247,0.001526324,0.0002766023,0.00007174151],"category_scores_gemma":[0.00007347782,0.0003098295,0.00007049371,0.0002687267,0.0003521201,0.0003615147,0.0006193317,0.001094458,0.000004788862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006689486,"about_ca_system_score_gemma":0.0001820002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001276537,"about_ca_topic_score_gemma":0.000005769496,"domain_scores_codex":[0.9982301,0.0002598044,0.0005314178,0.0004783138,0.0002558013,0.0002445357],"domain_scores_gemma":[0.9973967,0.0002460888,0.0007265277,0.001298156,0.0002834788,0.00004903236],"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":[0.0001965223,0.0002598671,0.00001348037,0.0006059154,0.00007861829,0.00004270766,0.0006088978,0.0152653,0.1417678,0.06581569,0.000008017075,0.7753372],"study_design_scores_gemma":[0.0005825477,0.000997767,0.000006424106,0.0007169914,0.00004106616,0.00003820309,0.00004293306,0.893647,0.07981116,0.02102616,0.002544533,0.0005451893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07284566,0.008132427,0.897338,0.0008136319,0.0001274974,0.0007343875,0.00001333819,0.0008905589,0.01910454],"genre_scores_gemma":[0.577116,0.001099123,0.4178529,0.0004487704,0.00003100657,0.00003923665,0.00007006551,0.0001108449,0.00323204],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8783817,"threshold_uncertainty_score":0.9999354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01716172760921064,"score_gpt":0.2755439220315536,"score_spread":0.2583821944223429,"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."}}