{"id":"W2153031840","doi":"10.1109/mmsp.2006.285290","title":"An Efficient Compression Scheme for Colour Filter Array Video Sequences","year":2006,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Demosaicing; Computer vision; Artificial intelligence; Computer science; Color filter array; Pixel; Bayer filter; Data compression; Coding (social sciences); Color gel; Mathematics; Image (mathematics); Image processing; Color image; Materials science","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":[0.0005394679,0.0001131464,0.0001344787,0.00006434067,0.0001944868,0.0002669063,0.0006173013,0.00005068475,0.00003262775],"category_scores_gemma":[0.0000207244,0.0000834992,0.0000629525,0.0001649144,0.0000402066,0.0002869784,0.00004886596,0.00006032056,0.00002347799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002025856,"about_ca_system_score_gemma":0.00003969976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009162493,"about_ca_topic_score_gemma":0.000005534326,"domain_scores_codex":[0.9988682,0.0001098248,0.0001886901,0.0003532859,0.0002122506,0.0002677161],"domain_scores_gemma":[0.9992182,0.0001722343,0.00005629939,0.0003818908,0.0001088875,0.00006247116],"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":[0.00002827166,0.0001947384,0.0001763686,0.00001786627,0.000005474112,0.00001037544,0.0001705912,0.004943282,0.9438437,0.03081289,0.01039004,0.009406437],"study_design_scores_gemma":[0.0006627369,0.0001983941,0.0009393231,0.0000255023,0.000004114782,0.00001179232,0.00001565559,0.4559378,0.523943,0.008569618,0.009443057,0.0002489986],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0378959,0.00005169061,0.9565241,0.0004497562,0.0002892981,0.0001845448,0.00000186826,0.0001743772,0.004428498],"genre_scores_gemma":[0.3587314,2.979494e-7,0.6395865,0.0004373137,0.0001000098,0.00001481173,0.000003465948,0.00000510572,0.001121064],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4509946,"threshold_uncertainty_score":0.3404998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02561751534079024,"score_gpt":0.3059639025197156,"score_spread":0.2803463871789253,"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."}}