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New Data Compression Method Reduces Big-Data Bottleneck; Outperforms, Enhances Jpeg

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UCLA researchers have compressed data using 'warping.'

A team of researchers at UCLA say they have developed a new method of data compression that outperforms existing techniques.

Credit: University of California, Los Angeles

University of California, Los Angeles (UCLA) researchers say they have developed a new method of data compression that outperforms existing techniques, such as JPEG for images, and that could eventually be integrated into medical, scientific, and video-streaming applications.

Their technique reshapes the signal carrying the data in a way that resembles the graphic art technique known as anamorphism, which creates optical illusions in art and film.

The researchers found that it is possible to achieve data compression by stretching and warping the data in a specific way according to a new mathematical function. The new function, called anamorphic stretch transform (AST), operates both in analog and digital domains. The researchers note that AST does not require prior knowledge of the data for the transformation to occur.

"Our transformation causes feature-selective stretching of the data and allocation of more pixels to sharper features where they are needed the most," says UCLA's Bahram Jalali.

In addition, AST can be used for image compression, as a standalone algorithm, or combined with existing digital compression techniques. "Reshaping the data by stretching and wrapping it in the prescribed manner compresses it without losing pertinent information," Jalali says.

From UCLA Newsroom (CA)
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Abstracts Copyright © 2013 Information Inc., Bethesda, Maryland, USA


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