Holographic Sensing

TL;DR

This paper introduces a least-squares based approach for designing holographic data representations that enable progressive, order-independent recovery, suitable for distributed storage and unreliable network transmission.

Contribution

It develops a novel least-squares framework for holographic representations, bridging the gap between classical estimation theory and holographic data encoding methods.

Findings

Provides a least-squares design methodology for holographic representations

Enables progressive data recovery independent of packet order

Applicable to stochastic data vectors in signal and image modeling

Abstract

Holographic representations of data encode information in packets of equal importance that enable progressive recovery. The quality of recovered data improves as more and more packets become available. This progressive recovery of the information is independent of the order in which packets become available. Such representations are ideally suited for distributed storage and for the transmission of data packets over networks with unpredictable delays and or erasures. Several methods for holographic representations of signals and images have been proposed over the years and multiple description information theory also deals with such representations. Surprisingly, however, these methods had not been considered in the classical framework of optimal least-squares estimation theory, until very recently. We develop a least-squares approach to the design of holographic representation for stochastic data vectors, relying on the framework widely used in modeling signals and images.