{"id":"W1483335260","doi":"10.1007/3-540-36228-2_64","title":"Inter-subband Redundancy Prediction Using Neural Network for Video Coding","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Codec; Redundancy (engineering); Wavelet; Coding (social sciences); Multiview Video Coding; Artificial neural network; Video compression picture types; Quality of service; Artificial intelligence; Real-time computing; Video quality; Video processing; Video tracking; Computer vision; Speech recognition; Computer network; 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":[0.0001788492,0.0003029491,0.0002370051,0.0002401123,0.0001345643,0.0002267429,0.0003842893,0.0004120869,0.001617531],"category_scores_gemma":[0.0004175649,0.0001352084,0.0001862284,0.0003668456,0.0001224168,0.0004122609,0.0001643438,0.0004627043,0.0003096661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003009111,"about_ca_system_score_gemma":0.0001959701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004226559,"about_ca_topic_score_gemma":0.006469504,"domain_scores_codex":[0.9999434,0.000009407559,0.000003525568,0.00001293029,0.0000224444,0.000008278329],"domain_scores_gemma":[0.9998862,0.00004329166,0.00000878112,0.00001167053,0.00004772161,0.000002393234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001970281,0.00009559091,0.0004693251,0.000132756,0.00004562551,0.00009443643,0.00003014946,0.2143182,0.06520127,0.005440006,0.003530222,0.7104453],"study_design_scores_gemma":[0.000003513154,0.00003017022,0.0002644617,0.000008081637,0.00001354567,0.00002604715,0.000003644187,0.9852064,0.01299861,0.0007630307,0.0006771359,0.000005253509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06071187,0.003573526,0.9277912,0.0002241789,0.0002472072,0.00004607143,0.0001429948,0.00106795,0.006195011],"genre_scores_gemma":[0.6712145,0.00225405,0.3124465,0.0001208245,0.0001250052,0.00007027072,0.0002927131,0.00008398361,0.01339221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004226559,"threshold_uncertainty_score":0.008403957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03613075393536967,"score_gpt":0.2831169653874189,"score_spread":0.2469862114520492,"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."}}