EAGER: GOALI: Bridging the Theory-Practice Divide in Multimedia Compression

EAGER：GOALI：弥合多媒体压缩的理论与实践鸿沟

• 批准号: 2008266
• 负责人: Aaron Wagner
• 金额: $ 4.29万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008266&HistoricalAwards=false
• 关键词: EAGER; GOALI; Bridging; Theory; Practice

Data compression is a central problem in engineering, both in a practical and conceptual sense. On the practical side, modern telecommunications would be impossible without sophisticated data compression algorithms, and improved compression schemes will lead to improved connectivity. On the conceptual side, since compressing a source amounts to learning its structure, data compression serves as a concrete problem with a well-defined objective that is connected to profound questions in AI. For compression of multimedia sources in particular, there has been relatively little interplay between the theoretical and experimental communities. This is arguably attributable to a lack of realism in the mathematical models used in theoretical studies and by a marked disconnection between the two communities. The GOALI project will develop new models of multimedia sources, the characterization and analysis of which will likely give rise to new theory. This project seeks to address both the mathematical realism in multimedia data models and the experimental validation of actual compression algorithms. The technical goal of the project is to develop improved mathematical models for multimedia sources by drawing on recent advances on the experimental side of the field, namely compression via deep neural networks (DNNs). This will be achieved via a collaborative project drawing together two PIs with expertise spanning theoretical and experimental approaches, helping to draw these two sides together. The project will also lead to new characterizations of the expressive power of neural networks for simulating random processes and new results connecting the problems of compression and generative modeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数据压缩是工程中的一个核心问题，无论是在实践还是概念上。 从实际角度来看，如果没有复杂的数据压缩算法，现代电信就不可能实现，而改进的压缩方案将改善连接性。 在概念方面，由于压缩源相当于学习其结构，因此数据压缩是一个具有明确目标的具体问题，与AI中的深刻问题有关。特别是对于多媒体源的压缩，理论界和实验界之间的相互作用相对较少。可以说，这是由于理论研究中使用的数学模型缺乏现实性，以及两个社区之间存在明显的脱节。GOALI项目将开发新的多媒体源模型，其特征和分析可能会产生新的理论。该项目旨在解决多媒体数据模型中的数学现实主义和实际压缩算法的实验验证。该项目的技术目标是通过利用该领域实验方面的最新进展，即通过深度神经网络（DNN）进行压缩，为多媒体源开发改进的数学模型。这将通过一个合作项目来实现，该项目将两个具有理论和实验方法专业知识的PI聚集在一起，帮助将这两个方面联系在一起。 该项目还将导致神经网络模拟随机过程的表达能力的新特征和连接压缩和生成建模问题的新结果。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Neural Networks Optimally Compress the Sawbridge

• DOI: 10.1109/dcc50243.2021.00022
• 发表时间: 2020-11
• 期刊: 2021 Data Compression Conference (DCC)
• 作者: Aaron B. Wagner;Johannes Ball'e