{"id":"W1523820344","doi":"10.1109/mwscas.2002.1186841","title":"Multiple description image coding using pixel interleaving and wavelet transform","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Interleaving; Pixel; Bitstream; Computer science; Encoder; Artificial intelligence; Computer vision; Coding (social sciences); Wavelet; Image quality; Wavelet transform; Algorithm; Quantization (signal processing); Image (mathematics); Pattern recognition (psychology); Decoding methods; Mathematics","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":[0.0004714682,0.0002691708,0.00032205,0.0004690277,0.0001548701,0.0004161774,0.0006605381,0.0004574053,0.0008309401],"category_scores_gemma":[0.001370343,0.0001623166,0.0002881281,0.0007088928,0.0003311959,0.0009364265,0.0007148685,0.0007036874,0.0002268871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002896277,"about_ca_system_score_gemma":0.0003419447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009751646,"about_ca_topic_score_gemma":0.0009460611,"domain_scores_codex":[0.9997312,0.00005045921,0.00001672783,0.00002308418,0.0001508318,0.00002758431],"domain_scores_gemma":[0.9995764,0.0001802626,0.00005160542,0.00008670334,0.00008594332,0.00001909618],"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":[0.0004351307,0.0001243352,0.001151955,0.0002674866,0.00006878309,0.0005842653,0.0001857716,0.1377554,0.1746184,0.1034212,0.002889858,0.5784975],"study_design_scores_gemma":[0.00005614114,0.000152623,0.0004473297,0.00002942297,0.00002910993,0.0005629031,0.00002383535,0.8976431,0.07898074,0.01602398,0.006017474,0.00003328959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01732292,0.0003682571,0.9808058,0.00009201171,0.00002792301,0.00004211819,0.00003247873,0.0002215486,0.001086973],"genre_scores_gemma":[0.3791856,0.0007157091,0.6169885,0.0000845507,0.00004443119,0.0001085622,0.0002121483,0.00004634362,0.002614243],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009751646,"threshold_uncertainty_score":0.002779782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03430065835188034,"score_gpt":0.2768928240940315,"score_spread":0.2425921657421511,"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."}}