{"id":"W3085622691","doi":"10.1109/tip.2021.3060803","title":"Affine Transformation-Based Deep Frame Prediction","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Image Processing","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Affine transformation; Computer science; Discrete cosine transform; Encoder; Inter frame; Transformation (genetics); Artificial neural network; Luminance; Artificial intelligence; Algorithm; Mean squared error; Frame (networking); Reference frame; Mathematics; Image (mathematics); Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003074164,0.0007803024,0.0005852277,0.000433536,0.000201002,0.0005393935,0.001455513,0.0005501935,0.001972008],"category_scores_gemma":[0.0006577411,0.0003397472,0.0005223898,0.0004676652,0.0002674387,0.0008902073,0.0004936692,0.001010793,0.000873776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007305639,"about_ca_system_score_gemma":0.0007482722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01441551,"about_ca_topic_score_gemma":0.01714264,"domain_scores_codex":[0.999819,0.00001648462,0.000009921635,0.00006069091,0.00006557647,0.00002840819],"domain_scores_gemma":[0.9998111,0.00003702509,0.0000298142,0.00003459689,0.00007610223,0.00001132473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002575931,0.0001191269,0.001295606,0.00005900473,0.00006771884,0.0001182398,0.00005410821,0.5525387,0.03462141,0.00763979,0.003483013,0.3997456],"study_design_scores_gemma":[0.000002852815,0.00001580013,0.0001048241,0.000002777292,0.000007307678,0.00001332004,0.000001555792,0.9947732,0.004109944,0.000497075,0.0004672487,0.000004060607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01897012,0.0002936062,0.9774184,0.0001065172,0.0000867472,0.00003166039,0.0001712585,0.001287104,0.001634676],"genre_scores_gemma":[0.6802343,0.0007832333,0.3053243,0.0001864666,0.0001180481,0.0001152424,0.0009753494,0.0001904986,0.01207256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01441551,"threshold_uncertainty_score":0.02866322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01922953221118374,"score_gpt":0.257720643789598,"score_spread":0.2384911115784143,"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."}}