{"id":"W2005330886","doi":"10.1109/ccece.2012.6334911","title":"A cost effective implementation of 8&amp;#x00D7;8 transform of HEVC from H.264/AVC","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Coding (social sciences); Context-adaptive variable-length coding; Scalable Video Coding; Adder; Field-programmable gate array; Multiview Video Coding; Context-adaptive binary arithmetic coding; Discrete cosine transform; Computer architecture; Decoding methods; Computer hardware; Video decoder; Coding tree unit; Algorithmic efficiency; Reuse; Video processing; Algorithm; Data compression; Motion compensation; Latency (audio); Artificial intelligence; Telecommunications; Video tracking","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":[0.0001060743,0.000330462,0.0002281022,0.0003412152,0.0002246701,0.0004055767,0.0007114582,0.0002888228,0.003436521],"category_scores_gemma":[0.0002307226,0.000152888,0.000261518,0.0002852891,0.0001500223,0.0005129121,0.0002243618,0.0003303939,0.0009992408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003274338,"about_ca_system_score_gemma":0.0005285877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001022852,"about_ca_topic_score_gemma":0.002382832,"domain_scores_codex":[0.9998436,0.00001410095,0.000008855347,0.00003225662,0.00007196467,0.00002924488],"domain_scores_gemma":[0.9998972,0.00001383751,0.00001509872,0.00002237814,0.00004488531,0.000006498934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002428937,0.0001412472,0.001163967,0.0003121447,0.00009018998,0.0004845806,0.0001274532,0.01074156,0.5043121,0.01443204,0.005372038,0.4625799],"study_design_scores_gemma":[0.0001716216,0.001966421,0.005026842,0.00007819672,0.0002536397,0.004230887,0.0001023399,0.16629,0.7398161,0.003918788,0.07807662,0.00006855105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.158377,0.001196457,0.8091114,0.0003173771,0.0002499524,0.0002394386,0.000166364,0.003351989,0.02698991],"genre_scores_gemma":[0.71068,0.0004394127,0.2713206,0.000175823,0.00007566252,0.0001007106,0.0003533239,0.00008312169,0.01677124],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003436521,"threshold_uncertainty_score":0.01149637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04279297388817564,"score_gpt":0.3378169425812518,"score_spread":0.2950239686930761,"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."}}