{"id":"W2141924422","doi":"10.1007/978-3-642-21593-3_12","title":"Structural Fidelity vs. Naturalness - Objective Assessment of Tone Mapped Images","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Naturalness; Tone mapping; Computer science; Fidelity; Measure (data warehouse); Tone (literature); Brightness; Artificial intelligence; High dynamic range; Range (aeronautics); Similarity (geometry); Similarity measure; Computer vision; Pattern recognition (psychology); Dynamic range; Image (mathematics); Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009228311,0.0003497435,0.0002390455,0.0008390153,0.0001318629,0.001023689,0.0002551567,0.0005294969,0.004439979],"category_scores_gemma":[0.005877741,0.0001510882,0.0002347132,0.0003011706,0.000445454,0.001100452,0.0005828608,0.0003920212,0.0004759112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001761193,"about_ca_system_score_gemma":0.0001342119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004210164,"about_ca_topic_score_gemma":0.0006362673,"domain_scores_codex":[0.9994862,0.00009191562,0.00002930404,0.00005671513,0.0002994787,0.00003628574],"domain_scores_gemma":[0.997544,0.001405819,0.000268371,0.0001518606,0.000516174,0.0001136988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002453894,0.000240471,0.01482044,0.001493843,0.0001688094,0.0002920722,0.0005732743,0.01153389,0.5323178,0.00494722,0.001105445,0.430053],"study_design_scores_gemma":[0.000168261,0.00614365,0.2446541,0.0006084581,0.0006215337,0.005473773,0.001498119,0.1831136,0.5340217,0.01359408,0.009813972,0.0002888884],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7022085,0.002618773,0.2715198,0.0002323064,0.0002007672,0.00034622,0.000525088,0.000356152,0.02199236],"genre_scores_gemma":[0.9654336,0.0009512361,0.02913918,0.00005118447,0.00007300446,0.00005865072,0.0002078895,0.00008844777,0.003996913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004439979,"threshold_uncertainty_score":0.01485324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01601109477786206,"score_gpt":0.2957736832610235,"score_spread":0.2797625884831615,"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."}}