{"id":"W2620564951","doi":"10.1002/sdtp.11838","title":"75‐2: <i>Invited Paper</i> : Large Scale Subjective Evaluation of Display Stream Compression","year":2017,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Qualcomm (Canada); York University","funders":"","keywords":"Lossless compression; Codec; Computer science; Compression (physics); Data compression; Scale (ratio); Encoding (memory); Lossy compression; Computer graphics (images); Computer vision; Artificial intelligence; Computer hardware; Geography; Materials science; Cartography","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":[0.001497881,0.0002249685,0.0004314077,0.00008496083,0.0003473025,0.0001091667,0.001586852,0.0001728876,0.00002136847],"category_scores_gemma":[0.0002440495,0.0001901949,0.0002083857,0.0001642355,0.0002751614,0.0008640573,0.0007226668,0.0002253168,0.000005807861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001218449,"about_ca_system_score_gemma":0.0001042725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001805577,"about_ca_topic_score_gemma":0.0001177076,"domain_scores_codex":[0.9970675,0.0002714801,0.0005674154,0.0005382443,0.001196304,0.0003590634],"domain_scores_gemma":[0.9968437,0.0002042164,0.0006267852,0.001826483,0.0003759828,0.0001228172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003314353,0.0006002967,0.006914737,0.00004701063,0.00002943532,0.000002547431,0.0003515153,0.0001080678,0.9876162,0.002991767,0.000253046,0.001052207],"study_design_scores_gemma":[0.002703518,0.0005983092,0.462314,0.0004784146,0.000164137,0.000008695403,0.000205543,0.001952781,0.528334,0.0008682678,0.00182482,0.0005475361],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5079457,0.0003676551,0.001997332,0.009303934,0.0008940411,0.00202925,0.0001366189,0.0004096282,0.4769159],"genre_scores_gemma":[0.9988145,0.00004884239,0.0007137714,0.0002629781,0.00003907745,0.00004498455,0.00001620011,0.00001399646,0.00004561782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4908689,"threshold_uncertainty_score":0.7755921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0215992723392266,"score_gpt":0.3249365403692598,"score_spread":0.3033372680300332,"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."}}