{"id":"W2041808434","doi":"10.1049/el:20082239","title":"Low-complexity 8×8 transform for image compression","year":2008,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Discrete cosine transform; Image compression; Transform coding; Discrete sine transform; Algorithm; Modified discrete cosine transform; Discrete Hartley transform; Data compression; Mathematics; Compression (physics); Discrete Fourier transform (general); Lapped transform; Computation; Matrix (chemical analysis); Reduction (mathematics); S transform; Image (mathematics); Arithmetic; Computer science; Fractional Fourier transform; Image processing; Computer vision; Fourier transform; Wavelet transform; Mathematical analysis; Discrete wavelet transform; 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":[0.00009704589,0.0001231921,0.0001146938,0.00006347637,0.0001954065,0.00009994499,0.0004769119,0.0000220572,0.000007973948],"category_scores_gemma":[0.000005223254,0.0001191826,0.00008942441,0.0001478945,0.00005139162,0.0009054605,0.00003209505,0.00008817697,0.00002196656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008807624,"about_ca_system_score_gemma":0.0000544572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004145041,"about_ca_topic_score_gemma":0.00000427136,"domain_scores_codex":[0.9988992,0.00002022063,0.0001816961,0.0002702427,0.000192104,0.0004365223],"domain_scores_gemma":[0.9995633,0.00004301995,0.0000485657,0.0002505429,0.00003302611,0.00006149922],"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":[0.00007572254,0.0002291682,0.00004754542,0.00007328053,0.0000426583,0.00002735655,0.001609189,0.00003742496,0.7225972,0.07547496,0.1050321,0.09475341],"study_design_scores_gemma":[0.002764253,0.0006344166,0.001147472,0.00002569934,0.00001154039,0.0001250783,0.00001152615,0.02064127,0.8818293,0.02330813,0.06878318,0.0007181777],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05262093,0.00003574429,0.9400826,0.005767636,0.0001256179,0.0003475835,0.000007350156,0.0001428213,0.0008697039],"genre_scores_gemma":[0.933227,0.00002992058,0.06028131,0.006058848,0.00009071177,0.00006994684,0.00006762408,0.00002080662,0.0001538051],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8806061,"threshold_uncertainty_score":0.4860126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0334821053465562,"score_gpt":0.2659717262750284,"score_spread":0.2324896209284721,"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."}}