{"id":"W2147790834","doi":"10.1109/ccece.1993.332379","title":"A noise resistant HDTV compression scheme","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Discrete cosine transform; High-definition television; Data compression; Computer science; Coding (social sciences); Image compression; Transform coding; Pixel; Algorithm; Compression ratio; Data compression ratio; Compression (physics); Computer vision; Bit rate; Artificial intelligence; Mathematics; Image processing; Image (mathematics); Computer hardware; Telecommunications; Engineering; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008602756,0.0001190982,0.0001286956,0.0001109898,0.0001803485,0.0001398745,0.001248547,0.00007482532,0.0002186113],"category_scores_gemma":[0.00005045208,0.00008692303,0.00005453829,0.0003395106,0.00004308327,0.000222341,0.000609698,0.000146939,0.0003822656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001739222,"about_ca_system_score_gemma":0.00000599539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001059489,"about_ca_topic_score_gemma":0.000001131175,"domain_scores_codex":[0.9989362,0.00002983469,0.0001752586,0.0003447311,0.0002628124,0.0002511964],"domain_scores_gemma":[0.9989055,0.00005279004,0.00005473421,0.0008797646,0.00003900174,0.00006820213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001202634,0.0003047207,0.0005865158,0.00002609161,0.00001899608,0.00007137903,0.0004430898,0.0000334947,0.1149942,0.3648105,0.2643136,0.2543854],"study_design_scores_gemma":[0.001028912,0.000274111,0.001540223,0.0002370247,0.000005169374,0.00003634587,0.0001459905,0.5904291,0.1483769,0.02219053,0.2349208,0.0008149051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06110386,0.002914684,0.7075562,0.01804061,0.0008115246,0.0003160779,0.000001998172,0.009036959,0.2002181],"genre_scores_gemma":[0.9099448,0.00006830251,0.08359966,0.0003418975,0.00001879562,0.00001396292,2.095423e-7,0.000005602069,0.006006753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.848841,"threshold_uncertainty_score":0.4913378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03828860280535067,"score_gpt":0.2309705698173804,"score_spread":0.1926819670120297,"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."}}