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Multispectral image compression methods for improvement of both colorimetric and spectral accuracy

[+] Author Affiliations
Wei Liang, Zhaolin Xiao

Xi’an University of Technology, School of Computer Science and Engineering, No. 5 Jinhua South Road, Xi’an 710048, China

Ping Zeng

Xidian University, School of Computer Science and Technology, No. 2 Taibai South Road, Xi’an 710071, China

Xi’an Shiyou University, School of Computer Science, No. 18 Second Dianzi Road, Xi’an 710065, China

Kun Xie

Xidian University, School of Computer Science and Technology, No. 2 Taibai South Road, Xi’an 710071, China

J. Electron. Imaging. 25(4), 043026 (Aug 15, 2016). doi:10.1117/1.JEI.25.4.043026
History: Received January 25, 2016; Accepted July 22, 2016
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Abstract.  We propose that both colorimetric and spectral distortion in compressed multispectral images can be reduced by a composite model, named OLCP(W)-X (OptimalLeaders_Color clustering-PCA-W weighted-X coding). In the model, first the spectral–colorimetric clustering is designed for sparse equivalent representation by generating spatial basis. Principal component analysis (PCA) is subsequently used in the manipulation of spatial basis for spectral redundancy removal. Then error compensation mechanism is presented to produce predicted difference image, and finally combined with visual characteristic matrix W, and the created image is compressed by traditional multispectral image coding schemes. We introduce four model-based algorithms to explain their validity. The first two algorithms are OLCPWKWS (OLC-PCA-W-KLT-WT-SPIHT) and OLCPKWS, in which Karhunen–Loeve transform, wavelet transform, and set partitioning in hierarchical trees coding are applied for the created image compression. And the latter two methods are OLCPW-JPEG2000-MCT and OLCP-JPEG2000-MCT. Experimental results show that, compared with the corresponding traditional coding, the proposed OLCPW-X schemes can significantly improve the colorimetric accuracy of rebuilding images under various illumination conditions and generally achieve satisfactory peak signal-to-noise ratio under the same compression ratio. And OLCP-X methods could always ensure superior spectrum reconstruction. Furthermore, our model has excellent performance on user interaction.

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© 2016 SPIE and IS&T

Citation

Wei Liang ; Ping Zeng ; Zhaolin Xiao and Kun Xie
"Multispectral image compression methods for improvement of both colorimetric and spectral accuracy", J. Electron. Imaging. 25(4), 043026 (Aug 15, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.4.043026


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