{"title":"Feature Reduction on Fuzzy SVM-Based Coding Unit Decision in HEVC","authors":"Ei Ei Tun, S. Aramvith, Y. Miyanaga","doi":"10.1109/ICEAST.2018.8434467","DOIUrl":null,"url":null,"abstract":"This paper proposes a feature reduction approach on a fuzzy SVM-based Coding Unit (CU) size decision method in a recent video encoding standard, High Efficiency Video Coding (HEVC). The proposed feature reduction approach with Rate Control (RC) can reduce computational complexity by eliminating some correlated features of a fuzzy SVM-based CU size decision method under a similar coding efficiency. According to the empirical results, our approach can achieve up to 3% of Time Saving (TS) under the same RD performance over a fuzzy SVM-based approach.","PeriodicalId":138654,"journal":{"name":"2018 International Conference on Engineering, Applied Sciences, and Technology (ICEAST)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Engineering, Applied Sciences, and Technology (ICEAST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEAST.2018.8434467","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper proposes a feature reduction approach on a fuzzy SVM-based Coding Unit (CU) size decision method in a recent video encoding standard, High Efficiency Video Coding (HEVC). The proposed feature reduction approach with Rate Control (RC) can reduce computational complexity by eliminating some correlated features of a fuzzy SVM-based CU size decision method under a similar coding efficiency. According to the empirical results, our approach can achieve up to 3% of Time Saving (TS) under the same RD performance over a fuzzy SVM-based approach.