Rate-distortion (R-D) function, a key quantity in information theory, characterizes the fundamental limit of how much a data source can be compressed subject to a fidelity criterion, by any compression algorithm. As researchers push for ever-improving compression performance, establishing the R-D function of a given data source is not only of scientific interest, but also sheds light on the possible room for improving compression algorithms. Previous work on this problem relied on distributional assumptions on the data source (Gibson, 2017) or only applied to discrete data (Blahut, 1972; Arimoto, 1972). By contrast, this paper makes the first attempt at an algorithm for sandwiching the R-D function of a general (not necessarily discrete) source requiring only i.i.d. data samples. We estimate R-D sandwich bounds for a variety of artificial and real-world data sources, in settings far beyond the feasibility of any known method, and shed light on the optimality of neural data compression (Ballé et al., 2021; Yang et al., 2022). Our R-D upper bound on natural images indicates theoretical room for improving state-of-the-art image compression methods by at least one dB in PSNR at various bitrates. Our data and code can be found at https://github.com/mandt-lab/empirical-RD-sandwich.
On the Nonasymptotic Bounds of Joint Source-Channel Coding with Hierarchical Sources
East China Normal University（华东师范大学）；Xihua University（西华大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Shanghai Jiao Tong University（上海交通大学）
Training-Free Rate-Distortion-Perception Traversal With Diffusion
Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong（信息工程系，香港中文大学，香港）；College of Electronic and Information Engineering, Shenzhen University, Shenzhen（电子与信息工程学院，深圳大学，深圳）
Towards Better Statistical Understanding of Watermarking LLMs
Imperial College Business School, Imperial College London（帝国理工学院商学院，帝国理工学院）；The University of Sydney Business School, The University of Sydney（悉尼大学商学院，悉尼大学）；Grado Department of Industrial and Systems Engineering, Virginia Tech（弗吉尼亚理工大学工业与系统工程系）
Distortion Resilience for Goal-Oriented Semantic Communication
Department of Information Convergence Engineering, Pusan National University（信息融合工程系，釜山国立大学）；Wireless Communications Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University（无线通信研究组，电子工程学院， Ton Duc Thang 大学）；College of Information Science and Electronic Engineering, Zhejiang University（信息科学与电子工程学院，浙江大学）；School of Computer Science and Statistics, Trinity College Dublin, The University of Dublin（计算机科学与统计学系，三一学院，都柏林大学）
