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UCLA Researchers Develop New Compression Method for Big Data
A team of researchers from the UCLA Henry Samueli School of Engineering and Applied Science have developed a new data compression method that could help with the capture and analysis of massive amounts of data in real time.
The technique, called "anamorphic stretch transform" or AST uses a newly developed mathematical function to stretch and warp data to compress it without losing pertinent information.
"Our transformation causes feature-selective stretching of the data and allocation of more pixels to sharper features where they are needed the most," said Mohammad Asghari, a member of the research team, in a prepared statement. "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 can compress analog and digital data. "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," according to information from UCLA. In digital applications, "the transformation causes the signal to be reshaped is such a way that 'sharp' features — its most defining characteristics — are stretched more than data's 'coarse' features."
AST originated from another technology, called time stretch dispersive Fourier transform, which was developed by the same research team. That technology slows down and amplifies faint but very fast signals so they can be detected and digitized in real time. However, high speed instruments created with that technology produce "a fire hose of data" that overwhelms even the most advanced computers. The team developed AST to compress that massive volume of data for real time capture and analysis.
The AST technique could eventually be used for medical and scientific research, as well as high speed video streaming. For example, to detect rare cancer cells in blood, researchers must screen millions of cells in a high-speed flow stream. AST could make it possible for medical researchers to screen more blood cells in real time. It could also be used for image compression, either on its own or in combination with existing digital compression techniques, to improve speed, quality or compression. According to information from the university, "AST can outperform standard JPEG image compression format, with dramatic improvement in terms of image quality and compression factor."
The research was published in the journal, Applied Optics. 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.
Leila Meyer is a technology writer based in British Columbia. She can be reached at firstname.lastname@example.org.