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Compressing industrial computed tomography images by means of contour coding

[+] Author Affiliations
Haina Jiang

Chongqing University, ICT Research Center, Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing 400044, China

College of Electronic Engineering, Chongqing 401331, China

Li Zeng

Chongqing University, ICT Research Center, Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing 400044, China

Chongqing University, College of Mathematics and Statistics, Chongqing 401331, China

J. Electron. Imaging. 22(4), 043017 (Nov 18, 2013). doi:10.1117/1.JEI.22.4.043017
History: Received July 15, 2013; Revised October 13, 2013; Accepted October 22, 2013
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Abstract.  An improved method for compressing industrial computed tomography (CT) images is presented. To have higher resolution and precision, the amount of industrial CT data has become larger and larger. Considering that industrial CT images are approximately piece-wise constant, we develop a compression method based on contour coding. The traditional contour-based method for compressing gray images usually needs two steps. The first is contour extraction and then compression, which is negative for compression efficiency. So we merge the Freeman encoding idea into an improved method for two-dimensional contours extraction (2-D-IMCE) to improve the compression efficiency. By exploiting the continuity and logical linking, preliminary contour codes are directly obtained simultaneously with the contour extraction. By that, the two steps of the traditional contour-based compression method are simplified into only one. Finally, Huffman coding is employed to further losslessly compress preliminary contour codes. Experimental results show that this method can obtain a good compression ratio as well as keeping satisfactory quality of compressed images.

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Citation

Haina Jiang and Li Zeng
"Compressing industrial computed tomography images by means of contour coding", J. Electron. Imaging. 22(4), 043017 (Nov 18, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.4.043017


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