{"id":"W45057407","doi":"","title":"Low-Band-Shifted Hierarchical Backward Motion Estimation, Compensation for Wavelet-Based Video Coding.","year":2002,"lang":"en","type":"article","venue":"Indian Conference on Computer Vision, Graphics and Image Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Motion compensation; Motion estimation; Quarter-pixel motion; Computer science; Wavelet; Inter frame; Reference frame; Artificial intelligence; Computer vision; Wavelet transform; Block-matching algorithm; Quantization (signal processing); Algorithm; Mathematics; Frame (networking); Video processing; Telecommunications; Video tracking","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004369252,0.0003785446,0.000368894,0.0005882414,0.0007434167,0.001372913,0.0007971904,0.0001778252,0.00001811259],"category_scores_gemma":[0.0001014305,0.0003534345,0.00008854703,0.0006415134,0.0002573885,0.00169903,0.0001711753,0.0003867245,0.00000963502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004151326,"about_ca_system_score_gemma":0.00008615095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003599867,"about_ca_topic_score_gemma":0.000002128073,"domain_scores_codex":[0.9973733,0.0001299036,0.0005806555,0.0009761549,0.0004899488,0.0004499835],"domain_scores_gemma":[0.9978406,0.0003051958,0.0003632553,0.0006145903,0.0006333203,0.0002430714],"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":[0.0000281561,0.0002057717,0.00002944111,0.0002106605,0.000007361779,0.00001298886,0.000423694,0.00004634562,0.0009884504,0.02570706,0.001045718,0.9712943],"study_design_scores_gemma":[0.0008755866,0.0004030805,0.001056517,0.0007432362,0.000007033853,0.00001766617,0.000005612937,0.9609855,0.006099803,0.0289636,0.0004146872,0.0004277114],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004442658,0.0000587049,0.9912257,0.00298459,0.0001587653,0.0005657625,0.0000284029,0.0004725761,0.00006286103],"genre_scores_gemma":[0.5580108,0.00003725592,0.4409027,0.0008685974,0.00005485417,0.00004286137,0.00005683861,0.0000193996,0.000006653399],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9708666,"threshold_uncertainty_score":0.9998918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0267959281425011,"score_gpt":0.2867228533230879,"score_spread":0.2599269251805869,"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."}}