{"id":"W4220870497","doi":"10.1109/icce53296.2022.9730460","title":"Deep Learning-Based HDR Image Upscaling Approach for 8K UHD Applications","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Consumer Electronics (ICCE)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Telus (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"High dynamic range; Computer science; Deep learning; Artificial intelligence; Residual; Computer vision; Multimedia; Computer graphics (images); Dynamic range; Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007225348,0.0002997906,0.0002498899,0.0003709737,0.0006449482,0.0003614006,0.002386961,0.00006632851,0.0005252198],"category_scores_gemma":[0.00005846308,0.0003520826,0.0001838038,0.0004410841,0.00008860572,0.0003409912,0.0003084292,0.0008705336,0.00004361121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006374319,"about_ca_system_score_gemma":0.0004839846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001530553,"about_ca_topic_score_gemma":0.000008960831,"domain_scores_codex":[0.9970089,0.0001778855,0.0004443543,0.0008981739,0.0008780061,0.0005926394],"domain_scores_gemma":[0.99832,0.0002117001,0.0003116606,0.0006203769,0.0004455614,0.00009071456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002326397,0.001311747,0.0001975934,0.00006343232,0.0003589392,0.00001202153,0.0003891283,0.01645475,0.06162899,0.8139514,0.006881185,0.09851823],"study_design_scores_gemma":[0.0006735231,0.0003046322,0.000009213558,0.000007313943,0.00001999441,0.00001075386,0.00005475599,0.9024245,0.02590303,0.006248686,0.06392804,0.0004155346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003556556,0.0002308641,0.9874068,0.001487262,0.0003393215,0.001043565,0.00003223971,0.0005096272,0.008594657],"genre_scores_gemma":[0.8741102,0.0001193395,0.1156655,0.001204765,0.0001323062,0.005850794,0.0004644768,0.00006181019,0.002390811],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8859698,"threshold_uncertainty_score":0.9998931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03149384909518813,"score_gpt":0.3054671157361049,"score_spread":0.2739732666409168,"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."}}