Learning Content-Weighted Pseudocylindrical Representation for 360° Image Compression

IF 10.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Image Processing Pub Date : 2024-10-17 DOI:10.1109/tip.2024.3477356

Mu Li, Youneng Bao, Xiaohang Sui, Jinxing Li, Guangming Lu, Yong Xu

引用次数: 0

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

学习内容加权伪圆柱表示法以实现 360° 图像压缩

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

IEEE Transactions on Image Processing 工程技术-工程：电子与电气

CiteScore

20.90

自引率

6.60%

发文量

774

审稿时长

7.6 months

期刊介绍： The IEEE Transactions on Image Processing delves into groundbreaking theories, algorithms, and structures concerning the generation, acquisition, manipulation, transmission, scrutiny, and presentation of images, video, and multidimensional signals across diverse applications. Topics span mathematical, statistical, and perceptual aspects, encompassing modeling, representation, formation, coding, filtering, enhancement, restoration, rendering, halftoning, search, and analysis of images, video, and multidimensional signals. Pertinent applications range from image and video communications to electronic imaging, biomedical imaging, image and video systems, and remote sensing.

期刊最新文献

Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching NTK-Guided Few-Shot Class Incremental Learning Learning Content-Weighted Pseudocylindrical Representation for 360° Image Compression Improved MRF Reconstruction via Structure-Preserved Graph Embedding Framework