Arthur C. Depoian, Ethan R. Adams, Aidan G. Kurz, Colleen P. Bailey, P. Guturu, K. Namuduri
{"title":"基于熵的图像压缩研究进展","authors":"Arthur C. Depoian, Ethan R. Adams, Aidan G. Kurz, Colleen P. Bailey, P. Guturu, K. Namuduri","doi":"10.1109/MetroCon56047.2022.9971134","DOIUrl":null,"url":null,"abstract":"The future of image compression is abundant with the opportunities recently developed through the application of advanced neural network algorithms configured to take into account multiple image parameters. This progress has spurred on further progression into more complex architectures to extract the feature of the image for optimal compression. Of the many models available, this work tracks an evolution of end to end image compression by first analyzing BLS2017 and its successors, BMSHJ2018 and MS2020.","PeriodicalId":292881,"journal":{"name":"2022 IEEE MetroCon","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Recent Advances in Entropy Based Image Compression\",\"authors\":\"Arthur C. Depoian, Ethan R. Adams, Aidan G. Kurz, Colleen P. Bailey, P. Guturu, K. Namuduri\",\"doi\":\"10.1109/MetroCon56047.2022.9971134\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The future of image compression is abundant with the opportunities recently developed through the application of advanced neural network algorithms configured to take into account multiple image parameters. This progress has spurred on further progression into more complex architectures to extract the feature of the image for optimal compression. Of the many models available, this work tracks an evolution of end to end image compression by first analyzing BLS2017 and its successors, BMSHJ2018 and MS2020.\",\"PeriodicalId\":292881,\"journal\":{\"name\":\"2022 IEEE MetroCon\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE MetroCon\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MetroCon56047.2022.9971134\",\"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 IEEE MetroCon","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MetroCon56047.2022.9971134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recent Advances in Entropy Based Image Compression
The future of image compression is abundant with the opportunities recently developed through the application of advanced neural network algorithms configured to take into account multiple image parameters. This progress has spurred on further progression into more complex architectures to extract the feature of the image for optimal compression. Of the many models available, this work tracks an evolution of end to end image compression by first analyzing BLS2017 and its successors, BMSHJ2018 and MS2020.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
