{"title":"基于非线性进化变换的图像编码","authors":"Seishi Takamura, A. Shimizu","doi":"10.1109/DCC.2013.100","DOIUrl":null,"url":null,"abstract":"Transform is one of the most important tools for image/video coding technology. In this paper, novel nonlinear transform generation based on genetic programming is proposed and implemented into H.264/AVC and HEVC reference software to enhance coding performance. The transform procedure itself is coded and transmitted. Despite this overhead, 0.590% (vs. JM18.0) and 1.711% (vs. HM5.0) coding gain was observed in our preliminary experiment.","PeriodicalId":388717,"journal":{"name":"2013 Data Compression Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Image Coding Using Nonlinear Evolutionary Transforms\",\"authors\":\"Seishi Takamura, A. Shimizu\",\"doi\":\"10.1109/DCC.2013.100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Transform is one of the most important tools for image/video coding technology. In this paper, novel nonlinear transform generation based on genetic programming is proposed and implemented into H.264/AVC and HEVC reference software to enhance coding performance. The transform procedure itself is coded and transmitted. Despite this overhead, 0.590% (vs. JM18.0) and 1.711% (vs. HM5.0) coding gain was observed in our preliminary experiment.\",\"PeriodicalId\":388717,\"journal\":{\"name\":\"2013 Data Compression Conference\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-03-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Data Compression Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DCC.2013.100\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Data Compression Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCC.2013.100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image Coding Using Nonlinear Evolutionary Transforms
Transform is one of the most important tools for image/video coding technology. In this paper, novel nonlinear transform generation based on genetic programming is proposed and implemented into H.264/AVC and HEVC reference software to enhance coding performance. The transform procedure itself is coded and transmitted. Despite this overhead, 0.590% (vs. JM18.0) and 1.711% (vs. HM5.0) coding gain was observed in our preliminary experiment.