{"id":"W2018648011","doi":"10.1109/mmsp.2011.6093787","title":"Image quality assessment based on multiple watermarking approach","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada; Université du Québec en Outaouais","funders":"","keywords":"Digital watermarking; Watermark; Computer science; Artificial intelligence; Robustness (evolution); Computer vision; Image quality; Wavelet; Weighting; Embedding; Pattern recognition (psychology); Image (mathematics)","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":[0.0006811539,0.0005574243,0.0005340371,0.001831193,0.0002284071,0.000743967,0.0005857381,0.0005283603,0.001278795],"category_scores_gemma":[0.002068784,0.0002094762,0.0005596565,0.0007955378,0.0004126063,0.001168756,0.0006803478,0.0004549593,0.0003272278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004273024,"about_ca_system_score_gemma":0.0001951986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004398492,"about_ca_topic_score_gemma":0.0004472858,"domain_scores_codex":[0.9990461,0.0001465209,0.00005109922,0.0001441886,0.0005632858,0.00004868251],"domain_scores_gemma":[0.9990507,0.0002194061,0.0001992967,0.00008658059,0.0004040041,0.00004009993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006054267,0.0001457809,0.004898215,0.0005479957,0.0002067904,0.0005519114,0.0002675127,0.07392912,0.3250293,0.01264566,0.0008928099,0.5802796],"study_design_scores_gemma":[0.00004568828,0.0006685947,0.005599183,0.0000522842,0.0001463293,0.001380526,0.00008903704,0.8839152,0.09875523,0.006280992,0.002965399,0.0001015745],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07388878,0.00117389,0.9208419,0.0001319941,0.00006348928,0.0001138264,0.00004189651,0.0006200492,0.003124076],"genre_scores_gemma":[0.7340389,0.001094535,0.2621698,0.00003963061,0.00007650927,0.00007640384,0.00007862884,0.00005229644,0.002373367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001831193,"threshold_uncertainty_score":0.004277945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06713375920712832,"score_gpt":0.302574671291822,"score_spread":0.2354409120846936,"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."}}