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Near-lossless data compression

Non-uniform sampling greatly improves storage and bandwidth utilization

Communications Software & Information Technology

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Image: Qef, via Wikimedia Commons

Even with the decreased cost and increased availability of both bandwidth and storage space, transferring and saving images and video is still time-consuming. Data compression methods such as JPEG, JPEG 2000, or MPEG-4, and others are bandwidth and space hogs, and as we flow more robust data over networks, we need better compression techniques.

The Shannon sampling theorem dictates that any signal needs to be sampled at the Nyquist rate (2x the maximum cut off frequency at which the bandwidth of the signal tends towards zero). The theorem has guided data compression approaches for years, but it is theoretically valid only for a stationary signal whose second-order statistics do not vary with time. In real life most signals are non-stationary, and the spectral content, the center frequency, and the bandwidth of the local signal vary with time. Given this, researchers have attempted several approaches to sub-sample the signal below the Nyquist rate exploiting the property of time-varying spectral characteristics of the signal. Although these attempts have resulted in some improvement in reduced sampling for a class of signals, they have not produced a lossless compression scheme or even near-lossless compression with a significant reduction in sampling rates.

Naval scientists have overcome these obstacles with a way to non-uniformly sample signals below the Nyquist rate without many of the associated aliasing artifacts from previous approaches as the signal can be uniquely reconstructed with negligible, (below noise level), errors using linear interpolation. This compression method can be used in combination with other compression methods.

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