{"id":"W2102498100","doi":"10.1109/tip.2008.2010638","title":"Low Bit-Rate Image Compression via Adaptive Down-Sampling and Constrained Least Squares Upconversion","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Oversampling; Computer science; Artificial intelligence; Computer vision; Sampling (signal processing); Image compression; Quantization (signal processing); JPEG; Image quality; Image resolution; Data compression; Algorithm; Image processing; Filter (signal processing); Image (mathematics); Telecommunications; Bandwidth (computing)","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":[0.000379646,0.0004208638,0.0003835158,0.0005345512,0.0001933206,0.0004792362,0.0006277762,0.0004034898,0.0009583817],"category_scores_gemma":[0.001280418,0.0002392772,0.000368937,0.0006030087,0.000460967,0.0006127949,0.0006603833,0.0005983299,0.0003728678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002031122,"about_ca_system_score_gemma":0.0002644616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008952112,"about_ca_topic_score_gemma":0.001161004,"domain_scores_codex":[0.9996498,0.00006461927,0.00001600358,0.00005276381,0.0001965473,0.00002035761],"domain_scores_gemma":[0.9996125,0.0001326369,0.00004550381,0.000115919,0.00008117162,0.00001228245],"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":[0.0003774457,0.000145461,0.00128735,0.0002009328,0.00005078789,0.0003792525,0.0002522159,0.05139538,0.4294247,0.01765063,0.001482715,0.4973532],"study_design_scores_gemma":[0.00004705732,0.0002129265,0.00167081,0.00002634923,0.00004340298,0.0008508747,0.00005323162,0.6530292,0.3334857,0.003710984,0.006820325,0.00004926089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06396063,0.0005741505,0.9319718,0.000157339,0.00004512982,0.00006516842,0.00003376112,0.0005322608,0.002659806],"genre_scores_gemma":[0.3219557,0.0005910862,0.6734792,0.00009643655,0.00004585441,0.00006733082,0.00009642598,0.000099041,0.003568876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009583817,"threshold_uncertainty_score":0.003206134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02180480612335261,"score_gpt":0.2812334426766468,"score_spread":0.2594286365532942,"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."}}