{"title":"基于SVM的高效HEVC内部预测","authors":"Jia-Kai Liu, Yinyi Lin","doi":"10.1109/GCCE46687.2019.9015477","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a fast and efficient inter mode decision using support vector machine (SVM) for HEVC inter prediction. In the proposed algorithm, we use information such as MV variance, neighboring CU depth level and coded block flag (CBF) as features for SVM-based CU size decision; while MV variance, SKIP flag and RDO ratio for SVM-based PU mode decision. The experimental results reveal that the proposed algorithm can achieve average 29.6% reduction of computation time with 0.09% bit rate increment only, compared to the original HEVC inter prediction.","PeriodicalId":303502,"journal":{"name":"2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Efficient HEVC Inter Prediction using SVM\",\"authors\":\"Jia-Kai Liu, Yinyi Lin\",\"doi\":\"10.1109/GCCE46687.2019.9015477\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a fast and efficient inter mode decision using support vector machine (SVM) for HEVC inter prediction. In the proposed algorithm, we use information such as MV variance, neighboring CU depth level and coded block flag (CBF) as features for SVM-based CU size decision; while MV variance, SKIP flag and RDO ratio for SVM-based PU mode decision. The experimental results reveal that the proposed algorithm can achieve average 29.6% reduction of computation time with 0.09% bit rate increment only, compared to the original HEVC inter prediction.\",\"PeriodicalId\":303502,\"journal\":{\"name\":\"2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GCCE46687.2019.9015477\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCCE46687.2019.9015477","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we propose a fast and efficient inter mode decision using support vector machine (SVM) for HEVC inter prediction. In the proposed algorithm, we use information such as MV variance, neighboring CU depth level and coded block flag (CBF) as features for SVM-based CU size decision; while MV variance, SKIP flag and RDO ratio for SVM-based PU mode decision. The experimental results reveal that the proposed algorithm can achieve average 29.6% reduction of computation time with 0.09% bit rate increment only, compared to the original HEVC inter prediction.