{"id":"W4388010227","doi":"10.1016/j.nima.2023.168829","title":"Efficient compression at the edge for real-time data acquisition in a billion-pixel X-ray camera","year":2023,"lang":"en","type":"article","venue":"Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique","funders":"Canada Research Chairs","keywords":"Computer science; Lossy compression; Data compression; Image compression; Artificial intelligence; Pixel; Quantization (signal processing); Frame rate; Lossless compression; Compression ratio; Data compression ratio; Computer vision; Real-time computing; Computer hardware; Image processing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003936827,0.0007075942,0.0004298265,0.0006881875,0.0004155667,0.0009146458,0.000769363,0.0006971423,0.005403507],"category_scores_gemma":[0.001739518,0.0003524944,0.000222695,0.000915294,0.0003788497,0.001154253,0.001081103,0.0007612699,0.001430413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003865068,"about_ca_system_score_gemma":0.000784414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008736115,"about_ca_topic_score_gemma":0.001754308,"domain_scores_codex":[0.9995808,0.0000428154,0.000034532,0.00006034877,0.0002353052,0.00004615118],"domain_scores_gemma":[0.9991671,0.0003034537,0.00006199644,0.0001859816,0.0002280477,0.00005346258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001551087,0.0001907896,0.002237998,0.0003070191,0.00004899171,0.0003647345,0.0003831188,0.008025109,0.4248967,0.008664556,0.008302417,0.5450274],"study_design_scores_gemma":[0.00009451458,0.0003951222,0.004377226,0.00009268915,0.00005697021,0.001373706,0.0001629622,0.333757,0.6305187,0.004978379,0.02410927,0.00008357124],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1535722,0.001430486,0.830655,0.0007369859,0.0002720022,0.00021791,0.0004932067,0.005087337,0.007534833],"genre_scores_gemma":[0.3218414,0.0006447734,0.669561,0.0003349212,0.0001198746,0.0001944272,0.0007704988,0.0003941015,0.00613907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005403507,"threshold_uncertainty_score":0.0180766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1023167078523791,"score_gpt":0.4427899032555492,"score_spread":0.3404731954031701,"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."}}