{"id":"W2133251102","doi":"10.1109/tip.2005.864165","title":"Quality-aware images","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":291,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Image quality; Computer vision; Digital watermarking; Quantization (signal processing); Decoding methods; Image (mathematics); Pattern recognition (psychology); Image processing; Transform coding; Information hiding; Wavelet; Wavelet transform; Discrete cosine transform; Algorithm","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":[0.0007142265,0.0005422966,0.0004742567,0.0009134266,0.0002426991,0.001759906,0.001210805,0.0006545394,0.002101538],"category_scores_gemma":[0.003217498,0.000330364,0.0004038673,0.0005786598,0.0009507961,0.003203694,0.0009889723,0.001103881,0.0008258566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004739388,"about_ca_system_score_gemma":0.0003573833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005109931,"about_ca_topic_score_gemma":0.0005177603,"domain_scores_codex":[0.9990858,0.0001082724,0.00004043055,0.0001962569,0.0005074061,0.00006189352],"domain_scores_gemma":[0.9982312,0.0002999116,0.0002375675,0.0005586661,0.000584085,0.00008870867],"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":[0.0003618624,0.0001190414,0.001834771,0.000607512,0.0001070946,0.0003867952,0.0004245445,0.04353653,0.237248,0.2252933,0.005550533,0.4845301],"study_design_scores_gemma":[0.000100997,0.0008623959,0.004066398,0.0001910874,0.0002648389,0.002841927,0.0002398389,0.4573314,0.3130897,0.119271,0.1015142,0.000226184],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01808251,0.001304482,0.9732056,0.0003251687,0.0001357765,0.00007695855,0.00008027907,0.0007330754,0.006056195],"genre_scores_gemma":[0.4467511,0.002566914,0.5417432,0.0004573227,0.0002924968,0.00008933929,0.0002559029,0.0002682343,0.007575327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002101538,"threshold_uncertainty_score":0.007030368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01561779813647675,"score_gpt":0.2873651539594971,"score_spread":0.2717473558230203,"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."}}