{"id":"W2041737230","doi":"10.1109/icip.2006.312505","title":"An Efficient MPEG2 to H.264 Half-Pixel Motion Compensation Transcoding","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Transcoding; Macroblock; Computer science; Pixel; Interpolation (computer graphics); Computer vision; Compensation (psychology); Motion compensation; Artificial intelligence; Quarter-pixel motion; Algorithm; Real-time computing; Motion (physics); Computer network; Decoding methods","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":[0.000205648,0.0001230368,0.0001180982,0.0002097311,0.0002023655,0.0002263518,0.000771829,0.00006613139,0.00001448428],"category_scores_gemma":[0.00001274856,0.0001054414,0.00004507963,0.000425747,0.00002039628,0.0002350496,0.00008885186,0.00008815171,0.00008440622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004260266,"about_ca_system_score_gemma":0.00001323641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000136095,"about_ca_topic_score_gemma":0.0000282827,"domain_scores_codex":[0.9988267,0.00004310459,0.0002117421,0.000401717,0.0002723025,0.0002443788],"domain_scores_gemma":[0.9992263,0.00003194285,0.00004475605,0.0005711323,0.00006512852,0.00006071963],"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":[0.000006640127,0.0002734249,0.0004000173,0.000009989669,0.000004069507,0.000004740674,0.0004100635,0.09750447,0.1237669,0.611789,0.001272985,0.1645577],"study_design_scores_gemma":[0.0003057983,0.0001623956,0.006960667,0.00003737511,0.000003865531,0.000009451092,0.0001230422,0.8580319,0.123775,0.008004314,0.002264159,0.0003220846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1834527,0.00001812338,0.8096768,0.002353866,0.000192384,0.0001221853,3.975578e-7,0.001328358,0.002855232],"genre_scores_gemma":[0.9273599,9.238467e-7,0.07223574,0.0001725976,0.00003569636,0.00001462388,0.000002310629,0.00000589915,0.0001722812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7605274,"threshold_uncertainty_score":0.4299777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01802085776590855,"score_gpt":0.2491041097275824,"score_spread":0.2310832519616738,"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."}}