CIF: Small: Collaborative Research: Combinatorial Joint Source-Channel Coding

有损/无损压缩和纠错码的算法一直是数字革命的核心。该项目专注于同时需要有损压缩和抗噪声能力的特定应用程序集。例如，在非完美半导体(闪存)存储器上存储高分辨率图像，以及在拥塞或噪声信道上进行实时视频监控。最先进的解决方案是“分离”：将现成的压缩算法与现成的纠错码串行级联。然而，正如研究人员最近所表明的那样，对于最坏的情况，分离的解远不是(甚至渐近)最优的。这为对联合信源-信道编码问题的组合、几何、代数和信息论方面的多方面研究提供了主要动机。数学的广度将吸引来自不同背景的年轻研究人员，并将有助于吸引新的人才进入该领域。创建具有可靠保证的复杂信源-信道码预计将在军事、空间探索、自然科学和消费应用方面产生技术影响。

Algorithms for lossy/lossless compression and error-correcting codes have been at the core of the digital revolution. This project focuses on the particular set of applications in which both lossy compression and noise resilience are required. Examples include storage of high resolution imagery on non-perfect semiconductor (flash) memory and real-time video surveillance over jammed or noisy channels.The state-of-the art solution is "separation": serial concatenation of an off-the-shelf compression algorithm with an off-the-shelf error-correcting code. However, as shown recently by the investigators, for worst-case guarantees the separated solution is far from being (even asymptotically) optimal. This provides the principal motivation for a multifaceted investigation of the combinatorial, geometric, algebraic and information theoretic aspects of the joint source-channel coding problem.The breadth of mathematics will be appealing to young researchers with a wide variety of backgrounds, and will help attract new talent to the field. Creation of sophisticated source-channel codes with dependable guarantees is expected to have technological impact in military, space exploration, natural science, and consumer applications.