{"id":"W2128185708","doi":"10.1109/icassp.2008.4517875","title":"Down-sampling in DCT domain using linear transform with double-sided multiplication for image/video transcoding","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Discrete cosine transform; Upsampling; Computer science; Transcoding; Computer vision; Interpolation (computer graphics); Nyquist–Shannon sampling theorem; Algorithm; Kernel (algebra); Computational complexity theory; Domain (mathematical analysis); Artificial intelligence; Transform coding; Multiplication (music); Mathematics; Image (mathematics); Discrete 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":[0.0004303049,0.0005093553,0.0003764255,0.0004985886,0.0002201068,0.0004147356,0.0004822189,0.0003466185,0.001866148],"category_scores_gemma":[0.0009865477,0.0001833442,0.0004742585,0.0004530991,0.0003120685,0.0005288462,0.0003416144,0.0005807992,0.0007333607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002307669,"about_ca_system_score_gemma":0.0003197729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007926622,"about_ca_topic_score_gemma":0.00130487,"domain_scores_codex":[0.9996847,0.0000583133,0.00002107304,0.00005088117,0.0001687271,0.00001630291],"domain_scores_gemma":[0.999712,0.00008593695,0.00002691663,0.00006855393,0.00009484752,0.00001166013],"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":[0.0001930317,0.00008810471,0.0005773426,0.0002646989,0.00004262107,0.0003018215,0.0001625343,0.05267191,0.328008,0.03602066,0.00240834,0.5792609],"study_design_scores_gemma":[0.00002904553,0.0002039013,0.0004962849,0.00003319458,0.00004139478,0.0009628848,0.00004717313,0.8091808,0.1657141,0.005023558,0.01823478,0.00003288441],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00595243,0.0001963889,0.9925663,0.00003828983,0.00002649145,0.0000291854,0.00001245752,0.0001765397,0.001001862],"genre_scores_gemma":[0.09834258,0.00049935,0.8985828,0.00006071179,0.00007240794,0.00006720504,0.00009629073,0.00006208281,0.002216602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001866148,"threshold_uncertainty_score":0.006242931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07380979543442248,"score_gpt":0.3378820509716416,"score_spread":0.2640722555372191,"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."}}