{"id":"W2010865325","doi":"10.1109/icip.2013.6738382","title":"Quantization table design revisited for image/video coding","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quantization (signal processing); Rate–distortion theory; Algorithm; Computer science; JPEG; Computational complexity theory; Coding (social sciences); Transform coding; Data compression; Theoretical computer science; Mathematics; Artificial intelligence; Discrete cosine transform; Image (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000237131,0.00009560212,0.0001175791,0.00008331009,0.0001210976,0.000270987,0.0006593053,0.00003647899,0.0001092457],"category_scores_gemma":[0.0002151208,0.00007926222,0.00002421285,0.0003127485,0.00001567136,0.00243757,0.0002217401,0.00004463885,0.00009380046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002162144,"about_ca_system_score_gemma":0.00002015581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001824711,"about_ca_topic_score_gemma":1.765014e-7,"domain_scores_codex":[0.9991145,0.00004392252,0.0001977128,0.0003127016,0.0001242806,0.0002068984],"domain_scores_gemma":[0.9988193,0.0002375213,0.00008697943,0.0005588207,0.0002411495,0.00005626214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004415134,0.0000354581,0.00002856423,0.00003520886,0.000005865239,0.000001052569,0.00004160244,0.000105921,0.1952269,0.2750047,0.4619422,0.06756821],"study_design_scores_gemma":[0.0001777277,0.00005562335,0.000045811,0.00004663249,0.000002137272,0.00000369738,0.000005949706,0.60351,0.3296327,0.05246291,0.01387126,0.000185626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001446702,0.0000354782,0.9969713,0.0004265804,0.0000545946,0.0007594277,0.000002411071,0.0008371741,0.0008985455],"genre_scores_gemma":[0.009346186,0.00002466209,0.9892166,0.0004608536,0.00002263534,0.0001824037,0.00001266674,0.000009977683,0.0007240172],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.603404,"threshold_uncertainty_score":0.3232219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03751100192820141,"score_gpt":0.3001948251372241,"score_spread":0.2626838232090227,"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."}}