{"id":"W2163673471","doi":"10.1109/itcc.2004.1286622","title":"Integrating compression with watermarking on video sequences","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Chrominance; Digital watermarking; Computer science; Computer vision; Data compression; Frame (networking); Artificial intelligence; Luminance; Intra-frame; Transform coding; Video processing; Wavelet transform; Synchronization (alternating current); Information hiding; Wavelet; Discrete cosine transform; Image (mathematics); Pixel; Channel (broadcasting); Telecommunications","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":[0.0002589007,0.0006637786,0.0003294359,0.0006610416,0.0001778107,0.0004897411,0.000416915,0.0005140888,0.00122529],"category_scores_gemma":[0.0009039342,0.0001719723,0.0003097444,0.0007505493,0.0003719846,0.000916319,0.0005053474,0.0003811106,0.000521219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001969252,"about_ca_system_score_gemma":0.0002291243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003995167,"about_ca_topic_score_gemma":0.000507497,"domain_scores_codex":[0.9997699,0.00003628918,0.00001449015,0.00003169917,0.0001252099,0.00002233278],"domain_scores_gemma":[0.9997898,0.0000794188,0.00002685493,0.00004246647,0.00005171143,0.000009827711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002442759,0.0000759932,0.0005450608,0.0004474774,0.00005439558,0.0005751291,0.0001067611,0.02786642,0.4904077,0.03516553,0.0008885264,0.4436229],"study_design_scores_gemma":[0.00006028532,0.0009998269,0.00196102,0.0001454108,0.0001374646,0.003051512,0.00004739536,0.3894093,0.5496711,0.02005463,0.0343962,0.00006586861],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03621937,0.002831889,0.9527397,0.0001882659,0.0003378669,0.0001496359,0.00004755797,0.000838316,0.006647402],"genre_scores_gemma":[0.3627107,0.004675859,0.6230292,0.0001583344,0.0005361458,0.0001346549,0.0001916331,0.00009834272,0.00846513],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00122529,"threshold_uncertainty_score":0.004099011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343873927381991,"score_gpt":0.2484186720128017,"score_spread":0.2349799327389817,"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."}}