Edge modeling prediction for computed tomography images

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Weinlich A, Amon P, Hutter A, Kaup A
Publication year: 2012
ISBN: 9781467344050


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.



FAU Authors / FAU Editors

Kaup, André Prof. Dr.-Ing.
Lehrstuhl für Multimediakommunikation und Signalverarbeitung


How to cite

APA:
Weinlich, A., Amon, P., Hutter, A., & Kaup, A. (2012). Edge modeling prediction for computed tomography images. 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.

BibTeX: 

Last updated on 2018-11-12 at 20:50