Compactly representing the visual signals is of fundamental importance in various image/video-centered applications. Although numerous approaches were developed for improving the image and video coding performance by removing the redundancies within visual signals, much less work has been dedicated to the transformation of the visual signals to another well-established modality for better representation capability. In this paper, we propose a new scheme for visual signal representation that leverages the philosophy of transferable modality. In particular, the deep learning model, which characterizes and absorbs the statistics of the input scene with online training, could be efficiently represented in the sense of rate-utility optimization to serve as the enhancement layer in the bitstream. As such, the overall performance can be further guaranteed by optimizing the new modality incorporated. The proposed framework is implemented on the state-of-the-art video coding standard (i.e., versatile video coding), and significantly better representation capability has been observed based on extensive evaluations.
End-to-End RGB-IR Joint Image Compression With Channel-wise Cross-modality Entropy Model
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；National Engineering Research Center of Visual Technology, School of Computer Science, Peking University（视觉技术国家工程研究中心，北京大学计算机科学学院）；School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）；Pengcheng Laboratory（鹏城实验室）；Advanced Institute of Information Technology, Peking University（北京大学信息科学技术高级研究院）
Leveraging Compression to Construct Transferable Bitrate Ladders
Laboratory for Image and Video Engineering (LIVE), The University of Texas at Austin（图像与视频工程实验室（LIVE），德克萨斯大学奥斯汀分校）；Colorado’s Laboratory for Image and Video Engineering (LIVE), University of Colorado Boulder（科罗拉多州图像与视频工程实验室（LIVE），科罗拉多大学波德分校）；Meta Platforms Inc.（Meta平台公司）
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
Charité - Universitätsmedizin Berlin（柏林查理医院）；Freie Universität Berlin（柏林自由大学）；Humboldt Universität zu Berlin（柏林洪堡大学）；Radboud University Medical Center（拉德伯德大学医学中心）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；Technical University of Munich（慕尼黑技术大学）
Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；College of Surveying and Geoinformatics, Tongji University（同济大学测绘与地理信息学院）；School of Computer and Information, Hefei University of Technology（合肥工业大学计算机与信息学院）
Joint Lossless Compression and Steganography for Medical Images via Large Language Models
Center for Future Media and School of Computer Science and Engineering, University of Electronic Science and Technology of China（未来媒体中心和电子科技大学计算机科学与工程学院）；Department of Computer Science and Engineering, University of Electronic Science and Technology of China（计算机科学与工程学院，电子科技大学）；Department of Electrical Engineering, and the Center for Intelligent Multidimensional Data Analysis, City University of Hong Kong（电子工程系和智能多维数据分析中心，城市大学）；Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence（机器学习系，Mohamed bin Zayed人工智能大学）
