{"id":"W2032426661","doi":"10.1109/icip.2010.5652059","title":"Rate-distortion optimal downsampling of H.264 compressed video using full-resolution information","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Blackberry (Canada); York University","funders":"","keywords":"Upsampling; Computer science; Encoder; Transcoding; Distortion (music); Benchmark (surveying); Image resolution; Sequence (biology); Rate–distortion theory; Video compression picture types; Artificial intelligence; Motion compensation; Algorithm; Computer vision; Data compression; Video processing; Video tracking; Image (mathematics); 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":[0.0005258602,0.0005352244,0.0005521572,0.0002743806,0.0001527031,0.0003828897,0.0003992275,0.0004018823,0.0006518706],"category_scores_gemma":[0.001513489,0.0002084263,0.0003101974,0.0002585003,0.0002267593,0.0005523257,0.0002819048,0.0003598546,0.000218982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002143019,"about_ca_system_score_gemma":0.0004083127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001223709,"about_ca_topic_score_gemma":0.001499214,"domain_scores_codex":[0.9997142,0.0000571387,0.00001634437,0.00004535796,0.0001438228,0.00002315505],"domain_scores_gemma":[0.9997575,0.0001097566,0.00003295831,0.00004149587,0.00004975024,0.000008582181],"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":[0.0006504479,0.0001453459,0.001207069,0.0003391743,0.0001040539,0.0005559414,0.0001258363,0.3261277,0.2025485,0.01149358,0.003149041,0.4535533],"study_design_scores_gemma":[0.00002448775,0.0002143492,0.0009896229,0.00001630963,0.00003916106,0.0005966391,0.00004325984,0.9247594,0.06803571,0.002769493,0.002488092,0.00002351171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07143319,0.001148003,0.9246305,0.0001402171,0.00005775014,0.00005485389,0.00009713043,0.0003947548,0.002043659],"genre_scores_gemma":[0.5155486,0.00118572,0.480344,0.00007643738,0.0001002179,0.00004257193,0.0003092907,0.00007769909,0.002315416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001223709,"threshold_uncertainty_score":0.002781034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02109864361960773,"score_gpt":0.2519298764700905,"score_spread":0.2308312328504828,"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."}}