{"title":"高光谱图像的压缩和隐私","authors":"B. Carpentieri","doi":"10.1109/ICECCME55909.2022.9988311","DOIUrl":null,"url":null,"abstract":"This paper presents a unified approach to the compression and privacy of Hyperspectral Images presenting a lossless compression method based on linear prediction and the application of watermarks to a Region of Interest (ROI) of an image. The proposed methods are experimentally evaluated.","PeriodicalId":202568,"journal":{"name":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Compression and Privacy of Hyperspectral Images\",\"authors\":\"B. Carpentieri\",\"doi\":\"10.1109/ICECCME55909.2022.9988311\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a unified approach to the compression and privacy of Hyperspectral Images presenting a lossless compression method based on linear prediction and the application of watermarks to a Region of Interest (ROI) of an image. The proposed methods are experimentally evaluated.\",\"PeriodicalId\":202568,\"journal\":{\"name\":\"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICECCME55909.2022.9988311\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECCME55909.2022.9988311","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a unified approach to the compression and privacy of Hyperspectral Images presenting a lossless compression method based on linear prediction and the application of watermarks to a Region of Interest (ROI) of an image. The proposed methods are experimentally evaluated.