All too frequently, the terms “data” and “information” are used interchangeably. The intent is generally “big information” rather than “big data.”  “Information” is consumed — not “data.” Yet networks today are designed to carry data, where a bit loss could result in significant loss of information. In contrast to data networking, every transmitted bit in an information network is an independent representation of the totality of information; a bit loss results in only a little information loss.

How then do we create bits that represent information rather than just data? Several researchers in Bell Labs are applying compressive sampling techniques to multimedia information transmission issues.

Compressive Sensing is a mathematical tool used to represent signals; in the case of multimedia, compressing video signals into “measurements.” The use of linear projections onto pseudo-random bases allows for far fewer numbers of measurements to represent a video than the number of pixels. Furthermore, compressive sensing of video exhibits such features as scalability with transmission bandwidth, adaptivity with application and robustness against noise, which makes it an ideal technology for use in information networks.

In one phase of this research, an imaging architecture for making compressive measurements, was created without using a lens. The architecture consists of an aperture assembly and a sensor. The aperture assembly consists of a two-dimensional array of aperture elements. The transmittance of each aperture element is independently controllable. The sensor is a single detection element. A compressive sensing matrix is implemented by adjusting the transmittance of the individual aperture elements according to the values of the sensing matrix. The device can be used for capturing images of visible and other spectra such as infrared, or millimeter waves, in surveillance applications for detecting anomalies or extracting features such as the speed of moving objects. Multiple sensors may be used with a single aperture assembly to capture multi-view images simultaneously. The prototype is built using a transparent monochrome liquid crystal display (LCD) screen and two photovoltaic sensors enclosed in a light tight box, as illustrated in Figure 1.

Figure 1 - Compressive Image Acquisition
Compressive image acquisition

Typically, pixel-by-pixel capture needs a large number of captures – with each capture resulting from one open element and containing data associated with only one pixel. In a compressive sampling approach, each capture results from many open elements, and contains information about many pixels. No physical image is formed. Instead, based on the collected information (rather than “data,”) a virtual image is defined mathematically. 

Although the lensless compressive imaging architecture was designed to address the need in camera networks, it may have far-reaching consequences in new classes of applications resulting from form-factor and cost reductions. For example, it may find applications in medical devices for embedded transmission of monitoring signals, and in providing far more sophisticated and new levels of analyses enabled by integrating the reconstruction of images and the anomaly detection processes from millions or billions of aperture devices.