{"id":"W2010183137","doi":"10.1109/pacrim.2007.4313300","title":"H.264 Intra Frame Coding and JPEG 2000-based Predictive Multiple Description Image Coding","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; JPEG; Coding (social sciences); Residual; Lossless JPEG; Transform coding; Artificial intelligence; Intra-frame; Algorithm; JPEG 2000; Data compression; Computer vision; Image compression; Image (mathematics); Image processing; Discrete cosine transform; Decoding methods; Mathematics; Statistics","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":[0.0006749813,0.0006404531,0.0004785654,0.001017156,0.0002137228,0.0004815214,0.001171577,0.0009185956,0.002224723],"category_scores_gemma":[0.001731171,0.0002111607,0.0003555649,0.0009548495,0.0003693248,0.0007459807,0.0004640475,0.001005646,0.001130041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005643903,"about_ca_system_score_gemma":0.001081997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004621832,"about_ca_topic_score_gemma":0.005052898,"domain_scores_codex":[0.9994408,0.0000616406,0.00002310196,0.00006623942,0.0003597703,0.00004834113],"domain_scores_gemma":[0.9993919,0.0001032977,0.00006799172,0.0001081252,0.0003000193,0.00002859264],"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":[0.0004333108,0.0001371394,0.000930765,0.0004540725,0.00006823554,0.0003719685,0.00006498946,0.03468022,0.06186176,0.03427924,0.02030747,0.8464109],"study_design_scores_gemma":[0.0001887072,0.0005603947,0.005981926,0.0001498055,0.0001413164,0.001659026,0.00006646996,0.6737438,0.16694,0.01206545,0.1383165,0.0001865772],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01246014,0.005755119,0.9680594,0.0004555799,0.0004885853,0.0003808344,0.0005339448,0.002056552,0.009809829],"genre_scores_gemma":[0.1626171,0.004671111,0.8030956,0.0005822832,0.0003848703,0.0004087146,0.0030554,0.0002205249,0.02496449],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004621832,"threshold_uncertainty_score":0.009189844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01871759974101272,"score_gpt":0.2699919687382236,"score_spread":0.2512743689972108,"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."}}