{"id":"W2154816270","doi":"10.1109/iscas.2007.377854","title":"Architecture for Multiple Reference Frame Variable Block Size Motion Estimation","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Macroblock; Computer science; Motion estimation; Reference frame; Block size; Block-matching algorithm; Frame rate; Throughput; Block (permutation group theory); Frame (networking); Architecture; Computer vision; Variable (mathematics); Image resolution; Artificial intelligence; Inter frame; Algorithm; Video processing; Mathematics; Video tracking; Geography; 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":[0.0003090129,0.0003551967,0.0002894772,0.0005631585,0.0002739088,0.0004789707,0.0009978057,0.0004847345,0.003533443],"category_scores_gemma":[0.0006209242,0.0002320424,0.0002121981,0.0003671259,0.0001418683,0.0005863916,0.0002928631,0.0004457076,0.001358091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003912122,"about_ca_system_score_gemma":0.0003881793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001880197,"about_ca_topic_score_gemma":0.002865222,"domain_scores_codex":[0.9997529,0.00004004122,0.00001574581,0.0000437495,0.0001158429,0.0000316734],"domain_scores_gemma":[0.9997851,0.0000387439,0.00002264597,0.00003789266,0.000105665,0.000009842175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004928256,0.0001115237,0.001798071,0.0003133272,0.0000847102,0.000389223,0.0002013561,0.03398102,0.3422732,0.02604242,0.008597476,0.5857149],"study_design_scores_gemma":[0.0001887403,0.0009785215,0.00373118,0.0001221383,0.0001686501,0.002129086,0.00008608103,0.6092057,0.3009035,0.005115819,0.0772569,0.0001137688],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02992804,0.0009996039,0.9580188,0.0002204953,0.0001237152,0.00009855237,0.0001022782,0.002839545,0.007668982],"genre_scores_gemma":[0.5368615,0.0008119391,0.447601,0.0002166891,0.0001181866,0.0001461826,0.0005167692,0.0001349927,0.01359264],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003533443,"threshold_uncertainty_score":0.01182055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02314581591950705,"score_gpt":0.2630561302895907,"score_spread":0.2399103143700837,"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."}}