{"id":"W1892195947","doi":"10.1109/iscas.2006.1692865","title":"An improved scalar quantization-based digital video watermarking scheme for H.264/AVC","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Digital watermarking; Computer science; Scalable Video Coding; Data compression; Quantization (signal processing); Watermark; Video quality; Robustness (evolution); Computer vision; Transcoding; Motion compensation; Real-time computing; Artificial intelligence; Computer network","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":[0.0004592223,0.0003954456,0.0004896324,0.0005240493,0.0003662457,0.0002935357,0.0005400715,0.0005683697,0.001645114],"category_scores_gemma":[0.0009123519,0.0001375464,0.0003214215,0.0004823856,0.0003094869,0.0008213267,0.0003603527,0.0005732811,0.0005444927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003217195,"about_ca_system_score_gemma":0.0004877476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001268549,"about_ca_topic_score_gemma":0.001713591,"domain_scores_codex":[0.9995381,0.00007217522,0.00003461625,0.00007134306,0.0002529978,0.00003076878],"domain_scores_gemma":[0.9996593,0.00004007808,0.00004597438,0.00005715066,0.0001788123,0.00001870506],"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":[0.0004516485,0.0001498125,0.0004407548,0.0002552058,0.000039718,0.0003947159,0.0001279926,0.0246327,0.6025365,0.01741718,0.00467146,0.3488823],"study_design_scores_gemma":[0.0001910748,0.001092389,0.001764596,0.00005719292,0.0001005253,0.001557686,0.00004513016,0.6845332,0.274193,0.003160518,0.03316632,0.0001382891],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05659011,0.002369298,0.9329906,0.0006191549,0.0005103074,0.0003177008,0.0001534262,0.001529398,0.004920032],"genre_scores_gemma":[0.4678668,0.001540695,0.5177292,0.0003288337,0.0002303038,0.0001117578,0.000360073,0.000057061,0.01177531],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001645114,"threshold_uncertainty_score":0.005503476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008321671336868406,"score_gpt":0.2434247969713371,"score_spread":0.2351031256344687,"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."}}