Adaptive and Network-Aware Source Coding

自适应和网络感知源编码

• 批准号: RGPIN-2018-04431
• 负责人: Linder, Tamas
• 金额: $ 9.32万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759042
• 关键词: Adaptive; Network; Aware; Source; Coding

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.

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，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构原始数据。随着社会大数据的激增和大数据革命的到来，源编码已经成为当代信息技术的关键组成部分，因为它方便了大量数据的高效存储和远程访问，以及通过互联网或无线网络等通信渠道高效传输含信息信号。拟议的研究将侧重于设计和分析新的源编码方法，以满足当代环境所带来的挑战，在这些环境中，通过不同容量的多个链路连接的多个用户希望通信或存储可能不固定和/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，以适应不断变化的源统计数据，并结合对内存、复杂性和延迟的实际约束，与更成熟的传统源编码理论形成对比，这些约束通常被忽视。研究目标分为两个主要主题：(1)系统研究单用户和分布式设置以及双向网络中信息源的最优零延迟或有限延迟码的性能和更精细的结构；(2)研究数据压缩与在线学习/分布式优化之间的丰富联系，以促进低复杂度、自适应序列方案的构建和分析，用于分布式环境下高度非平稳源的有损编码，如传感器网络的编码。拟议研究的培训部分每年将为2名硕士和3名博士学生提供刺激的研究挑战，并使他们沉浸在信息和通信技术的重要当前主题中。这项研究有望对有损源编码的基本原理及其实际应用（如通信网络中多媒体信号的传输）提供更深入的理解。