Adaptive and Network-Aware Source Coding

信源编码(数据压缩)的目标是给出数据(例如，文本、图像、音频、视频和其他非传统数据类型，例如图形数据)的紧凑表示，从中可以完美地或以感知上可接受的方式重建原始数据。随着社会中大数据集的激增和大数据革命的到来，信源编码已经成为当代信息技术的关键组成部分，因为它促进了大量数据的有效存储和远程访问，以及通过互联网或无线网络等通信渠道有效地传输承载信息的信号。拟议的研究将侧重于设计和分析新的信源编码方法，以应对当代环境带来的挑战，在当代环境中，通过不同容量的多个链路连接的几个用户希望通信或存储可能不是固定的和/或结构良好的数据。新的和实质性的研究挑战出现在方案的分析和设计中，这些方案适应于变化的信源统计并结合了对内存、复杂性和延迟的实际约束，这与更成熟的传统信源编码理论形成了对比，在传统的信源编码理论中，这些约束通常被忽略。*研究目标分为两个主题：(1)系统研究在单用户和分布式以及双向网络环境下信源的最优零或有限时延码的性能和精细结构；(2)研究数据压缩和在线学习/分布式优化之间的丰富联系，以便于构建和分析用于分布式环境中高度非平稳信源的低复杂度、自适应有损编码的序贯方案，例如传感器网络编码。*拟议研究的培训部分将提供2名理科硕士。和每年3名博士生，进行激动人心的研究挑战，让他们沉浸在当前重要的信息和通信技术主题中。这项研究有望加深对有损信源编码的基本原理及其实际应用方面的理解，例如多媒体信号在通信网络中的传输。

The goal of source coding (data compression) is to give a compact representation of data (e.g., text, image, audio, video, and other, non-traditional data types such as graphical data) from which the original data can be reconstructed either perfectly or in a perceptually acceptable manner. With the proliferation of large data sets in our society and the coming of the big data revolution, source coding has become a key component in contemporary information technologies as it facilitates the efficient storage and remote access of large volumes of data, as well as the efficient transmission of information-bearing signals over communication channels such as the Internet or wireless networks. The proposed research will focus on designing and analyzing novel source coding methods that meet the challenges posed by contemporary settings in which several users, connected via multiple links of different capacities, want to communicate or store data that may not be stationary and/or well structured. New and substantial research challenges arise in the analysis and design of schemes that adapt to changing source statistics and incorporate practical constraints on memory, complexity, and delay, contrasting the more established traditional theory of source coding where such constraints are usually disregarded. ******The research objectives are divided into two main themes: (1) The systematic study of the performance and finer structure of optimal zero- or limited-delay codes for information sources in the single user and distributed settings as well as for two-way networks; (2) The investigation of the rich connections between data compression and online learning/distributed optimization to facilitate the construction and analysis of low-complexity, adaptive sequential schemes for lossy coding of highly non-stationary sources in distributed settings, such as coding for sensor networks. ******The training component of the proposed research will provide 2 M.Sc. and 3 Ph.D. students each year with stimulating research challenges and immerse them in important current topics in information and communication technologies. The research is expected to provide a deeper understanding of the fundamental principles of lossy source coding as well as its practical aspects, such as the transmission of multimedia signals in communication networks.