{"title":"基于映射表的低计算复杂度鱼眼图像校正","authors":"Y. Ahn, Suk-ju Kang","doi":"10.1109/ISOCC.2016.7799760","DOIUrl":null,"url":null,"abstract":"In this paper, we proposed a mapping table-based fisheye image correction to reduce computation time. Specifically, the proposed algorithm uses the field of view correction model and camera coordinate conversion when performing an image interpolation for generating an image with the target image size. The experimental results show that the proposed algorithm reduces the computation time up to 15.85% while improving perceptual image quality, compared with the benchmark algorithm.","PeriodicalId":278207,"journal":{"name":"2016 International SoC Design Conference (ISOCC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Mapping table-based fisheye image correction for low computational complexity\",\"authors\":\"Y. Ahn, Suk-ju Kang\",\"doi\":\"10.1109/ISOCC.2016.7799760\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we proposed a mapping table-based fisheye image correction to reduce computation time. Specifically, the proposed algorithm uses the field of view correction model and camera coordinate conversion when performing an image interpolation for generating an image with the target image size. The experimental results show that the proposed algorithm reduces the computation time up to 15.85% while improving perceptual image quality, compared with the benchmark algorithm.\",\"PeriodicalId\":278207,\"journal\":{\"name\":\"2016 International SoC Design Conference (ISOCC)\",\"volume\":\"84 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International SoC Design Conference (ISOCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISOCC.2016.7799760\",\"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 International SoC Design Conference (ISOCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISOCC.2016.7799760","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mapping table-based fisheye image correction for low computational complexity
In this paper, we proposed a mapping table-based fisheye image correction to reduce computation time. Specifically, the proposed algorithm uses the field of view correction model and camera coordinate conversion when performing an image interpolation for generating an image with the target image size. The experimental results show that the proposed algorithm reduces the computation time up to 15.85% while improving perceptual image quality, compared with the benchmark algorithm.