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‘Warping’ Compresses Big Data

LOS ANGELES, Dec. 30, 2013 — A physics-based data compression method outperforms existing technologies, such as JPEG, for images and could eventually be adopted for the capture and analysis of massive amounts of data in real time for communication, scientific research and medicine.

The entirely new way to compress data was developed by a team at the University of California, Los Angeles, Henry Samueli School of Engineering and Applied Science led by Bahram Jalali, the Northrop Grumman Opto-Electronic Chair in Electrical Engineering.


UCLA researchers compressed data using 'warping.' 


The group discovered that it is possible to achieve data compression by stretching and warping the data in a specific fashion prescribed by a newly developed mathematical function. The technology, dubbed "anamorphic stretch transform," or AST, operates both in analog and digital domains. In analog applications, AST makes it possible to not only capture and digitize signals that are faster than the speed of the sensor and the digitizer, but also to minimize the volume of data generated in the process.

AST also can compress digital records and does not require prior knowledge of the data for the transformation to take place; it occurs naturally and in a streaming fashion.

"Our transformation causes feature-selective stretching of the data and allocation of more pixels to sharper features where they are needed the most," postdoctoral researcher Mohammad Asghari said. "For example, if we used the technique to take a picture of a sailboat on the ocean, our anamorphic stretch transform would cause the sailboat’s features to be stretched much more than the ocean, to identify the boat while using a small file size."

AST also can be used for image compression, as a stand-alone algorithm or combined with existing digital compression techniques to enhance speed or quality or to improve the amount that images can be compressed. Results have shown that AST can outperform the standard JPEG image compression format, with dramatic improvement in terms of image quality and compression factor.

AST has its origin in another technology pioneered by the Jalali group, time-stretch dispersive Fourier transform, which slows down and amplifies faint but very fast signals so they can be detected and digitized in real time.


UCLA's Mohammad Asghari (left) with professor Bahram Jalali.


High-speed instruments created with this technology enabled the discovery of optical rogue waves in 2007 and the detection of cancer cells in blood with one-in-a-million sensitivity in 2012. But these instruments produce a "fire hose" of data that overwhelms even the most advanced computers. The need to deal with such data loads motivated the UCLA team to search for a new data compression technology.

"Reshaping the data by stretching and wrapping it in the prescribed manner compresses it without losing pertinent information," Jalali said. "It emulates what happens to waves as they travel through physical media with specific properties. It also brings to mind aspects of surrealism and the optical effects of anamorphism."

The research was published in Applied Optics http://dx.doi.org/10.1364/AO.52.006735. Its application to digital image compression was presented this month at the IEEE Global Conference on Signal and Information Processing, and at the IEEE International Symposium on Signal Processing and Information Technology, where it received the conference’s best paper award.

For more information, visit: www.ucla.edu


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