{"id":"W4249667883","doi":"10.1109/icce.2002.1013935","title":"Robust transmission of JPEG 2000 images over noisy channels","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Bitstream; Computer science; JPEG; Channel (broadcasting); Convolutional code; Transmission (telecommunications); Quantization (signal processing); Lossless JPEG; Code (set theory); Transform coding; JPEG 2000; Data compression; Artificial intelligence; Computer vision; Image compression; Computer hardware; Decoding methods; Image (mathematics); Algorithm; Computer network; Image processing; Telecommunications; Discrete cosine transform","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":[0.0002417638,0.000351808,0.0003181851,0.0002623514,0.0001859527,0.0003836711,0.0003354512,0.0004393066,0.0007075628],"category_scores_gemma":[0.001202145,0.0001379382,0.0001838077,0.0002424376,0.0003336456,0.0004793943,0.0003617961,0.0003787198,0.0003268225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000193641,"about_ca_system_score_gemma":0.0002268841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004848807,"about_ca_topic_score_gemma":0.0005010128,"domain_scores_codex":[0.9998054,0.00002342365,0.000009097485,0.00002637966,0.0001130203,0.00002282508],"domain_scores_gemma":[0.9996129,0.0001235037,0.0001051821,0.00007426344,0.00006954683,0.00001453138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006159507,0.0000529209,0.000903418,0.0003121054,0.0000564125,0.0006179119,0.0001663447,0.03918964,0.7577,0.008212723,0.001917416,0.1902552],"study_design_scores_gemma":[0.00004960742,0.0005110909,0.00262094,0.00005885424,0.00009030329,0.001578704,0.00005204061,0.3258203,0.6591187,0.002665674,0.007382824,0.00005108187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2819607,0.00182058,0.7093433,0.000248416,0.0001287926,0.0000739964,0.0002093013,0.001236045,0.004978885],"genre_scores_gemma":[0.8850789,0.00126865,0.1079662,0.00008014082,0.00009899025,0.00004604928,0.0002973705,0.00007613371,0.005087524],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0007075628,"threshold_uncertainty_score":0.00236702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02165228797829119,"score_gpt":0.2626612406744573,"score_spread":0.2410089526961662,"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."}}