Edge modeling prediction for computed tomography images
Weinlich A, Amon P, Hutter A, Kaup A (2012)
Publication Language: English
Publication Status: Published
Publication Type: Conference contribution, Conference Contribution
Publication year: 2012
Article Number: 6410795
Event location: San Diego, California
ISBN: 9781467344050
DOI: 10.1109/VCIP.2012.6410795
Abstract
Predictive coding is applied in many state-of-the-art lossless image compression algorithms like JPEG-LS, CALIC, or least-squares-based methods. We propose a new approach for accurate intensity prediction in pixel-predictive coding of computed tomography (CT) images. Exploiting their particular edge characteristic, the method only relies on a small twelve-pixel context. It does neither require adaptation to larger-region image characteristics nor the transmission of side-information and therefore may be particularly suitable for compression of small images like in region-of-interest coding. While applying simple linear prediction with fixed weights in homogeneous regions, a Gauss error model-function is fit to given contexts in edge regions and then sampled at the position corresponding to the pixel to be predicted in order to obtain prediction values. By the example of CALIC, it is shown that for CT data the edge modeling prediction (EMP) approach can yield an even smaller prediction error than other methods relying on context modeling. © 2012 IEEE.
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How to cite
APA:
Weinlich, A., Amon, P., Hutter, A., & Kaup, A. (2012). Edge modeling prediction for computed tomography images. In Proceedings of the 2012 IEEE Visual Communications and Image Processing, VCIP 2012. San Diego, California, US.
MLA:
Weinlich, Andreas, et al. "Edge modeling prediction for computed tomography images." Proceedings of the 2012 IEEE Visual Communications and Image Processing, VCIP 2012, San Diego, California 2012.
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