{"id":"W2108373964","doi":"10.1142/s0219467809003551","title":"PECSI: A PRACTICAL PERCEPTUALLY-ENHANCED COMPRESSION FRAMEWORK FOR STILL IMAGES","year":2009,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Upsampling; Image compression; Computer science; Artificial intelligence; Computer vision; Quantization (signal processing); Image quality; Compression (physics); Data compression; Perception; Data compression ratio; Texture compression; Encoding (memory); Image (mathematics); Image processing","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":[0.0005535956,0.000790668,0.0004212439,0.0006367123,0.0002424891,0.0005733193,0.001037657,0.0005015274,0.003142535],"category_scores_gemma":[0.001193258,0.0002138392,0.0004069077,0.0003617513,0.000405907,0.0009080155,0.0008333332,0.001038264,0.0008639503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002491216,"about_ca_system_score_gemma":0.0004506703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008833758,"about_ca_topic_score_gemma":0.001310657,"domain_scores_codex":[0.9996087,0.00003988081,0.00001395749,0.00003836222,0.0002756131,0.0000235238],"domain_scores_gemma":[0.9996568,0.00009495088,0.00002961033,0.00006405817,0.000131214,0.00002345012],"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":[0.0003756416,0.0001228365,0.0004238741,0.0004170562,0.00007274697,0.0005167304,0.0001434384,0.07723706,0.2360863,0.03855121,0.008747496,0.6373056],"study_design_scores_gemma":[0.00005075753,0.0005035888,0.0008768313,0.00005558151,0.00004150954,0.001714708,0.00004211556,0.8380683,0.1127572,0.008491216,0.0373299,0.00006840589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003426489,0.0003158102,0.9935572,0.00004782584,0.00004391392,0.00007952064,0.00005649063,0.001003347,0.001469417],"genre_scores_gemma":[0.09364891,0.0008022508,0.900062,0.0001152906,0.0001093036,0.0001373859,0.0003981765,0.0002219362,0.004504707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003142535,"threshold_uncertainty_score":0.01051283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03312077197588812,"score_gpt":0.3944696626569971,"score_spread":0.3613488906811089,"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."}}