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Journal of Electronic Imaging

Multispectral image compression methods for improvement of both colorimetric and spectral accuracy
Author(s): Wei Liang; Ping Zeng; Zhaolin Xiao; Kun Xie
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Paper 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.

Paper Details

Date Published: 15 August 2016
PDF: 13 pages
J. Electron. Imag. 25(4) 043026 doi: 10.1117/1.JEI.25.4.043026
Published in: Journal of Electronic Imaging Volume 25, Issue 4
Show Author Affiliations
Wei Liang, Xi'an Univ. of Technology (China)
Ping Zeng, Xidian Univ. (China)
Xi’an Shiyou Univ. (China)
Zhaolin Xiao, Xi'an Univ. of Technology (China)
Kun Xie, Xidian Univ. (China)


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