{"id":"W1972127941","doi":"10.1117/1.jei.21.1.013011","title":"Combining distributed video coding with super-resolution to achieve H.264/AVC performance","year":2012,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation, Science and Economic Development Canada; Communications Research Centre Canada","funders":"McGill University","keywords":"Computer science; Codec; Upsampling; Encoder; Decoding methods; Algorithm; Context-adaptive binary arithmetic coding; Real-time computing; Data compression; Computer vision; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001050217,0.0001914643,0.000253671,0.0002493793,0.0002523874,0.0001844988,0.00077351,0.00002649369,0.000002573554],"category_scores_gemma":[0.00009315146,0.0001595199,0.00005636526,0.0006195938,0.00004635066,0.003909066,0.000176453,0.000601242,0.000005085641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005540157,"about_ca_system_score_gemma":0.0002599359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003319147,"about_ca_topic_score_gemma":8.358519e-7,"domain_scores_codex":[0.9980026,0.00005940985,0.000399889,0.000188363,0.0004240861,0.0009255981],"domain_scores_gemma":[0.998774,0.00007155796,0.0003762768,0.0002802798,0.0003172166,0.000180702],"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":[0.001450802,0.001155858,0.09951327,0.0003506953,0.0003789024,0.0002154054,0.01100237,0.007827327,0.3500917,0.0544343,0.007846258,0.4657332],"study_design_scores_gemma":[0.004518814,0.004556868,0.01223475,0.002889844,0.0001936707,0.01299517,0.0004922664,0.7044124,0.2072577,0.01052424,0.03740698,0.002517327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0406739,0.00100798,0.955757,0.002029782,0.0001376476,0.0001005739,4.869259e-7,0.0001395308,0.0001531051],"genre_scores_gemma":[0.7987204,0.00003863043,0.2007966,0.0002894941,0.0001178982,0.000004874782,8.764726e-7,0.00001735474,0.00001385767],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7580465,"threshold_uncertainty_score":0.6505034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007244783625771539,"score_gpt":0.2487016997272244,"score_spread":0.2414569161014528,"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."}}