{"id":"W3167381044","doi":"10.1109/ieeeconf51394.2020.9443427","title":"A Lightweight Model for Deep Frame Prediction in Video Coding","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Coding (social sciences); Convolutional neural network; Coding tree unit; Discrete cosine transform; Intra-frame; Frame (networking); Artificial intelligence; Transform coding; Data compression; Predictive coding; Algorithm; Computer engineering; Decoding methods; Pixel; Computer network; Image (mathematics); Mathematics","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":[0.0000926131,0.00009044297,0.000124557,0.00008621163,0.00007218778,0.00008819555,0.0006695797,0.00008783967,0.00000631276],"category_scores_gemma":[0.0001198392,0.00007469553,0.00004619054,0.0002988677,0.00001404258,0.0003261294,0.0002549156,0.0001250559,0.00001647163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002144437,"about_ca_system_score_gemma":0.00002355775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002889464,"about_ca_topic_score_gemma":0.000003604788,"domain_scores_codex":[0.9991401,0.000011334,0.0001892354,0.0003367778,0.0001275696,0.0001949524],"domain_scores_gemma":[0.9995493,0.00006808176,0.00003965061,0.000254641,0.00003319102,0.00005511255],"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":[0.00005957694,0.0001565425,0.00178092,0.0001131078,0.00002565446,0.0000115381,0.004698611,0.04887161,0.01699591,0.7236766,0.04350749,0.1601024],"study_design_scores_gemma":[0.0002528178,0.00006492325,0.00004480542,0.00001978125,0.000001373613,8.842262e-7,0.00003603047,0.9681937,0.00890344,0.02137964,0.001014662,0.00008791955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002587878,0.00006756603,0.9862583,0.008722935,0.0001214707,0.0001584374,0.000001447716,0.00107436,0.00100763],"genre_scores_gemma":[0.8641696,0.00001393198,0.1345331,0.0009088871,0.00002668394,0.00006615083,7.17914e-7,0.000005165322,0.0002757144],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9193221,"threshold_uncertainty_score":0.3045995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04331269346564982,"score_gpt":0.2500432350558368,"score_spread":0.2067305415901869,"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."}}