R. Kozhemiakin, S. Abramov, V. Lukin, Blaao Djurovic, I. Djurović
{"title":"Compression ratio prediction for DCT-based coder","authors":"R. Kozhemiakin, S. Abramov, V. Lukin, Blaao Djurovic, I. Djurović","doi":"10.1109/MSMW.2016.7538143","DOIUrl":null,"url":null,"abstract":"The paper deals with considering a problem typical for lossy compression of remote sensing images. While compressing an image under interest by DCT-based coders which are rather efficient it is desirable to predict what CR will be attained for a given quantization step. We show that such prediction is possible and it can be done easily, quickly, and quite accurately. Moreover, prediction can be done for practically noise-free images and images corrupted by unknown type of noise with unknown characteristics. Influence of noise type and image properties is briefly studied.","PeriodicalId":6504,"journal":{"name":"2016 9th International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves (MSMW)","volume":"16 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 9th International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves (MSMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MSMW.2016.7538143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The paper deals with considering a problem typical for lossy compression of remote sensing images. While compressing an image under interest by DCT-based coders which are rather efficient it is desirable to predict what CR will be attained for a given quantization step. We show that such prediction is possible and it can be done easily, quickly, and quite accurately. Moreover, prediction can be done for practically noise-free images and images corrupted by unknown type of noise with unknown characteristics. Influence of noise type and image properties is briefly studied.