{"id":"W2090251087","doi":"10.1145/1352012.1352019","title":"On the accuracy and complexity of rate-distortion models for fine-grained scalable video sequences","year":2008,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Scalability; Decoding methods; Distortion (music); Range (aeronautics); Encoding (memory); Set (abstract data type); Video quality; Sequence (biology); Algorithm; Frame (networking); Theoretical computer science; Channel (broadcasting); Data mining; Artificial intelligence; Bandwidth (computing)","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003562588,0.0001616539,0.0002015794,0.0001488356,0.001950058,0.00006144708,0.00172975,0.00007519303,0.000002457373],"category_scores_gemma":[0.0001736914,0.0001272326,0.00007104153,0.0004509858,0.0008335296,0.0001904623,0.0001474506,0.0002654435,0.00000278688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000024529,"about_ca_system_score_gemma":0.00005341254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007377975,"about_ca_topic_score_gemma":0.00002084988,"domain_scores_codex":[0.9988547,0.0001226072,0.0003626916,0.000345964,0.0001356616,0.0001784009],"domain_scores_gemma":[0.9928396,0.004471651,0.0002134413,0.002232411,0.000182031,0.00006084266],"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":[0.00002693063,0.0007004715,0.00006169786,0.00004950604,0.000077189,2.112012e-7,0.001815977,0.008176849,0.00287033,0.3029849,0.0004732372,0.6827627],"study_design_scores_gemma":[0.0004197033,0.0001193191,0.0004863541,0.00007506678,0.00001966891,0.000009733412,0.0001724673,0.8560625,0.004508915,0.1367578,0.00117401,0.0001944771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01339924,0.0002652226,0.974113,0.01097858,0.00003343136,0.0007525251,0.00003436134,0.00027077,0.0001527882],"genre_scores_gemma":[0.8164204,0.000553193,0.1824331,0.0001328731,0.000008574531,0.0004075314,0.00001058486,0.000007922958,0.0000257662],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8478856,"threshold_uncertainty_score":0.9993493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1165557361278968,"score_gpt":0.3093399843051858,"score_spread":0.192784248177289,"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."}}