{"id":"W2020664125","doi":"10.1109/icme.2012.175","title":"SSIM-Inspired Perceptual Video Coding for HEVC","year":2012,"lang":"en","type":"article","venue":"","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Normalization (sociology); Rate–distortion optimization; Coding (social sciences); Artificial intelligence; Video quality; Multiview Video Coding; Computer vision; Coding tree unit; Residual; Context-adaptive binary arithmetic coding; Data compression; Algorithmic efficiency; Algorithm; Decoding methods; Mathematics; Video processing; Video tracking; Statistics; Metric (unit)","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":[0.0005370898,0.0001041179,0.0001261734,0.0000474915,0.0001584924,0.000149818,0.0004417748,0.00004185513,0.00009008549],"category_scores_gemma":[0.00004830142,0.00008774297,0.00008397723,0.0001196659,0.00002295833,0.001162147,0.0001692423,0.00005559588,0.0001466241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000459927,"about_ca_system_score_gemma":0.00004057847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002960924,"about_ca_topic_score_gemma":0.000004766205,"domain_scores_codex":[0.9989308,0.00004536222,0.0001883466,0.0002069453,0.0001918989,0.0004366304],"domain_scores_gemma":[0.9992529,0.0001537551,0.00004254077,0.0003643868,0.00005610739,0.000130297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009526884,0.0002709265,0.001556458,0.00005381732,0.00003316278,0.000001865876,0.006483898,0.000006008504,0.007189003,0.8294744,0.04970277,0.1052182],"study_design_scores_gemma":[0.004552668,0.0008705894,0.02886442,0.0001062033,0.00007881098,0.00007190238,0.005086723,0.1638987,0.1196881,0.01409184,0.6601463,0.002543793],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004316851,0.00005632752,0.9839613,0.00227326,0.0005094417,0.0001939121,0.000001702391,0.0002054371,0.008481805],"genre_scores_gemma":[0.8063983,0.000004265531,0.1883541,0.002785719,0.0002846599,0.00003722155,0.000002922691,0.000007539366,0.002125249],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8153825,"threshold_uncertainty_score":0.3578055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06799974357950593,"score_gpt":0.3415459183526042,"score_spread":0.2735461747730983,"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."}}