{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002918736,0.0004562344,0.0002671714,0.000477988,0.0001544148,0.0003643974,0.0005863746,0.0003385217,0.001631104],"category_scores_gemma":[0.001109396,0.00009857633,0.0002650814,0.0005031866,0.0003169786,0.0004060997,0.0003953228,0.000561816,0.0004410309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004425137,"about_ca_system_score_gemma":0.0004367447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001600736,"about_ca_topic_score_gemma":0.00235241,"domain_scores_codex":[0.9997653,0.00003862316,0.00001156644,0.00002309406,0.0001504673,0.00001090332],"domain_scores_gemma":[0.9997641,0.00005042359,0.00002017391,0.00003945952,0.0001160217,0.000009828368],"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":[0.0001709094,0.00006107932,0.0003415199,0.0001801789,0.00003043878,0.0001337965,0.0001077525,0.1716803,0.2126719,0.05815172,0.004450817,0.5520195],"study_design_scores_gemma":[0.0000130344,0.0000858805,0.0004027169,0.00002693399,0.00001002462,0.0001828748,0.00001313135,0.9414102,0.04044821,0.008204925,0.00918149,0.00002067092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005806773,0.0003477146,0.991067,0.00007704734,0.00006508786,0.00004938023,0.00003487868,0.0002488547,0.00230325],"genre_scores_gemma":[0.281224,0.001171467,0.7094678,0.0002186569,0.0001320523,0.0002415084,0.0003105647,0.000163548,0.007070355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001631104,"threshold_uncertainty_score":0.005456626,"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."}}