{"title":"利用潜在特征引导和扩散先验实现极端图像压缩","authors":"Zhiyuan Li, Yanhui Zhou, Hao Wei, Chenyang Ge, Jingwen Jiang","doi":"10.1109/tcsvt.2024.3455576","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"42 1","pages":""},"PeriodicalIF":8.3000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards Extreme Image Compression with Latent Feature Guidance and Diffusion Prior\",\"authors\":\"Zhiyuan Li, Yanhui Zhou, Hao Wei, Chenyang Ge, Jingwen Jiang\",\"doi\":\"10.1109/tcsvt.2024.3455576\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":13082,\"journal\":{\"name\":\"IEEE Transactions on Circuits and Systems for Video Technology\",\"volume\":\"42 1\",\"pages\":\"\"},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Circuits and Systems for Video Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/tcsvt.2024.3455576\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Circuits and Systems for Video Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/tcsvt.2024.3455576","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
期刊介绍:
The IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) is dedicated to covering all aspects of video technologies from a circuits and systems perspective. We encourage submissions of general, theoretical, and application-oriented papers related to image and video acquisition, representation, presentation, and display. Additionally, we welcome contributions in areas such as processing, filtering, and transforms; analysis and synthesis; learning and understanding; compression, transmission, communication, and networking; as well as storage, retrieval, indexing, and search. Furthermore, papers focusing on hardware and software design and implementation are highly valued. Join us in advancing the field of video technology through innovative research and insights.