{"id":"W2096000848","doi":"10.1109/tip.2006.877414","title":"Lossless compression of VLSI layout image data","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Lossless compression; Huffman coding; Arithmetic coding; Data compression; Image compression; Entropy encoding; Computer science; Context-adaptive binary arithmetic coding; Lossy compression; Adaptive coding; Tunstall coding; Color Cell Compression; Texture compression; Algorithm; Artificial intelligence; Image processing; Image (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":[0.0001884167,0.0003942199,0.0003351731,0.0008984559,0.0002407758,0.0006517283,0.0007605152,0.0003536651,0.001703967],"category_scores_gemma":[0.001250426,0.0001299136,0.0001887969,0.00130692,0.0004454812,0.0008426218,0.0005503178,0.0004600997,0.0006655682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004426379,"about_ca_system_score_gemma":0.0003946791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006913695,"about_ca_topic_score_gemma":0.001056283,"domain_scores_codex":[0.9997222,0.00002318816,0.00001419905,0.00002904982,0.0001852697,0.00002608226],"domain_scores_gemma":[0.9995033,0.0001470137,0.00005633395,0.0001402935,0.000130108,0.00002293457],"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":[0.0004623961,0.0001254253,0.001036324,0.0001928448,0.00003020726,0.0005913823,0.0001384816,0.07505432,0.2653768,0.03261709,0.007629198,0.6167455],"study_design_scores_gemma":[0.00007498456,0.0002918382,0.001270807,0.00003032211,0.00003318907,0.001050802,0.00005670051,0.5866252,0.3860791,0.009375408,0.01506561,0.00004602023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1364809,0.0009375143,0.8520635,0.0004252545,0.0001845045,0.00008895685,0.0004621801,0.002604919,0.006752308],"genre_scores_gemma":[0.6016776,0.0006701634,0.3895149,0.0003292614,0.0001492028,0.0001113012,0.001111201,0.0002626401,0.006173773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001703967,"threshold_uncertainty_score":0.00570035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02474841739913329,"score_gpt":0.2822287551718686,"score_spread":0.2574803377727353,"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."}}