{"title":"在压缩域中调整图像大小","authors":"J. Mukhopadhyay","doi":"10.1109/ISSCS.2017.8034942","DOIUrl":null,"url":null,"abstract":"Image resizing is used to convert an image of a given size to one of a different size. There exist different algorithms for resizing an image both in the spatial domain, as well as in the frequency domain where it is stored in compressed form. There are certain advantages of performing the resizing operation directly in the compressed domain. First, it saves the computational overhead of inverse and forward transforms. Next, by exploiting various properties of the transform domain, it is possible to design efficient fast algorithms providing good quality reconstructed image in the spatial domain. In this paper, we review a few algorithms for resizing an image by arbitrary sizes and provide a brief comparison of their performances.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Image resizing in the compressed domain\",\"authors\":\"J. Mukhopadhyay\",\"doi\":\"10.1109/ISSCS.2017.8034942\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image resizing is used to convert an image of a given size to one of a different size. There exist different algorithms for resizing an image both in the spatial domain, as well as in the frequency domain where it is stored in compressed form. There are certain advantages of performing the resizing operation directly in the compressed domain. First, it saves the computational overhead of inverse and forward transforms. Next, by exploiting various properties of the transform domain, it is possible to design efficient fast algorithms providing good quality reconstructed image in the spatial domain. In this paper, we review a few algorithms for resizing an image by arbitrary sizes and provide a brief comparison of their performances.\",\"PeriodicalId\":338255,\"journal\":{\"name\":\"2017 International Symposium on Signals, Circuits and Systems (ISSCS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Symposium on Signals, Circuits and Systems (ISSCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSCS.2017.8034942\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSCS.2017.8034942","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image resizing is used to convert an image of a given size to one of a different size. There exist different algorithms for resizing an image both in the spatial domain, as well as in the frequency domain where it is stored in compressed form. There are certain advantages of performing the resizing operation directly in the compressed domain. First, it saves the computational overhead of inverse and forward transforms. Next, by exploiting various properties of the transform domain, it is possible to design efficient fast algorithms providing good quality reconstructed image in the spatial domain. In this paper, we review a few algorithms for resizing an image by arbitrary sizes and provide a brief comparison of their performances.
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