{"title":"基于数据挖掘和决策树的快速H.264/AVC转HEVC转码器","authors":"G. Corrêa, L. Agostini, L. Cruz","doi":"10.1109/ISCAS.2016.7539110","DOIUrl":null,"url":null,"abstract":"High Efficiency Video Coding (HEVC) is gradually replacing its predecessor, the H.264/AVC standard, as the state-of-the-art technology for video compression. However, H.264/AVC has dominated the market for over a decade, so that there is an enormous amount of legacy content that must be migrated. This paper proposes a fast transcoder based on an extensive data mining process on H.264/AVC decoding attributes. The data mining allowed identifying relevant information from the H.264/AVC decoding process, which was conveyed to the C4.5 machine learning algorithm to build a set of decision trees that simplify the complex Coding Unit (CU) size decision in HEVC. Experimental results have shown an average reduction of 44% in the transcoding time, with a small bit rate increase of 1.67%. These results outperform any previous works available in the literature.","PeriodicalId":6546,"journal":{"name":"2016 IEEE International Symposium on Circuits and Systems (ISCAS)","volume":"2017 1","pages":"2539-2542"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Fast H.264/AVC to HEVC transcoder based on data mining and decision trees\",\"authors\":\"G. Corrêa, L. Agostini, L. Cruz\",\"doi\":\"10.1109/ISCAS.2016.7539110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"High Efficiency Video Coding (HEVC) is gradually replacing its predecessor, the H.264/AVC standard, as the state-of-the-art technology for video compression. However, H.264/AVC has dominated the market for over a decade, so that there is an enormous amount of legacy content that must be migrated. This paper proposes a fast transcoder based on an extensive data mining process on H.264/AVC decoding attributes. The data mining allowed identifying relevant information from the H.264/AVC decoding process, which was conveyed to the C4.5 machine learning algorithm to build a set of decision trees that simplify the complex Coding Unit (CU) size decision in HEVC. Experimental results have shown an average reduction of 44% in the transcoding time, with a small bit rate increase of 1.67%. These results outperform any previous works available in the literature.\",\"PeriodicalId\":6546,\"journal\":{\"name\":\"2016 IEEE International Symposium on Circuits and Systems (ISCAS)\",\"volume\":\"2017 1\",\"pages\":\"2539-2542\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Symposium on Circuits and Systems (ISCAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCAS.2016.7539110\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Symposium on Circuits and Systems (ISCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCAS.2016.7539110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast H.264/AVC to HEVC transcoder based on data mining and decision trees
High Efficiency Video Coding (HEVC) is gradually replacing its predecessor, the H.264/AVC standard, as the state-of-the-art technology for video compression. However, H.264/AVC has dominated the market for over a decade, so that there is an enormous amount of legacy content that must be migrated. This paper proposes a fast transcoder based on an extensive data mining process on H.264/AVC decoding attributes. The data mining allowed identifying relevant information from the H.264/AVC decoding process, which was conveyed to the C4.5 machine learning algorithm to build a set of decision trees that simplify the complex Coding Unit (CU) size decision in HEVC. Experimental results have shown an average reduction of 44% in the transcoding time, with a small bit rate increase of 1.67%. These results outperform any previous works available in the literature.