{"id":"W1570779175","doi":"10.1007/978-3-642-13681-8_41","title":"Error-Resilient and Error Concealment 3-D SPIHT Video Coding with Added Redundancy","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Set partitioning in hierarchical trees; Redundancy (engineering); Computer science; Coding (social sciences); Novelty; Error concealment; Artificial intelligence; Algorithm; Pattern recognition (psychology); Decoding methods; Wavelet; Mathematics; Wavelet transform; Statistics; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003533291,0.000694287,0.0003716305,0.0006608187,0.0002058442,0.0005280038,0.0007192351,0.000802654,0.002156199],"category_scores_gemma":[0.001207273,0.000305593,0.0004800197,0.0006339173,0.0004338727,0.0009956089,0.000617286,0.0006813399,0.0006480199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003997173,"about_ca_system_score_gemma":0.0003209221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006631134,"about_ca_topic_score_gemma":0.001052831,"domain_scores_codex":[0.9997322,0.00004016917,0.00001479899,0.00001965426,0.0001721966,0.00002092548],"domain_scores_gemma":[0.9994254,0.000203182,0.00007198535,0.0001593271,0.0001287763,0.00001131706],"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":[0.0004949117,0.00008256287,0.0005104305,0.0003570414,0.00008795397,0.0006257293,0.0003257947,0.2223382,0.3226124,0.08388991,0.003988829,0.3646862],"study_design_scores_gemma":[0.00001862143,0.0001614388,0.0004669173,0.00006750037,0.00004180479,0.001189594,0.00002493523,0.8079015,0.1741159,0.01044345,0.00552502,0.00004326092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03234399,0.0006298777,0.9614154,0.0001596525,0.0001039195,0.00003211979,0.0001181727,0.0005780783,0.004618811],"genre_scores_gemma":[0.4484234,0.001538972,0.5308899,0.0001654361,0.0001195425,0.00007487668,0.0003472484,0.0002080855,0.01823251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002156199,"threshold_uncertainty_score":0.007213175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01799140709486215,"score_gpt":0.2760827457279089,"score_spread":0.2580913386330468,"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."}}