CIF:Small:Toward a Modern Theory of Compression: Manifold Sources and Learned Compressors

Cornell University, Ithaca NY

Investigators

Abstract

Past improvements in our communications infrastructure have been fueled primarily by increasing our ability to move bits around the world. In contrast, there has been less attention paid to how data compression can be used to reduce the number of bits that must be communicated to begin with. Recent advances in machine learning suggest that the number of bits that we must send to sustain a teleconference or stream a video is significantly lower than previously thought. Reducing the number of bits that must be transmitted would enable more realistic and immersive telepresence. This, in turn, may lead to a reduced need for travel, including reduced commuting. The resulting benefits would be manifold, including reduced carbon emissions, reduced spread of infectious disease, and more flexible arrangements for those balancing work with child- or elder-care. This project will contribute to the grand challenge of achieving transparent, secure, inexpensive communications with a full range of telepresence capabilities. The impetus for this project is the exemplary performance obtained by Artificial Neural Network-based compressors on multimedia sources. Although these compressors have attractive empirical performance, there is little theory to explain their performance or point the way toward future improvements. The project will develop a modern theory of data compression that will help explain why these compressors perform as well as they do, estimate their degree of suboptimality, and point the way toward how they can be improved. New theory will be developed focusing on the two most salient aspects of these compressors, namely their ability to apply nonlinear transforms to the data and the fact that they are learned from source samples. The project will also increase the external visibility of researchers and results in information theory and lead to more diverse workforce through the support of female and veteran Ph.D. students. 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.