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

Wei Liang; Ping Zeng; Zhaolin Xiao; Kun Xie

doi:10.1117/1.JEI.25.4.043026

15 August 2016 Multispectral image compression methods for improvement of both colorimetric and spectral accuracy

Wei Liang, Ping Zeng, Zhaolin Xiao, Kun Xie

Author Affiliations +

Funded by: Xi’an University of Technology, China National Science Foundation, Shaanxi Province of China, State Key Laboratory of Virtual Reality Technology and Systems, Xidian University, Science and Technology Project of Xi'an, Science Technology Project of Xi’an

Journal of Electronic Imaging, Vol. 25, Issue 4, 043026 (August 2016). https://doi.org/10.1117/1.JEI.25.4.043026

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.

Citation Download Citation

Wei Liang, Ping Zeng, Zhaolin Xiao, and Kun Xie "Multispectral image compression methods for improvement of both colorimetric and spectral accuracy," Journal of Electronic Imaging 25(4), 043026 (15 August 2016). https://doi.org/10.1117/1.JEI.25.4.043026

Published: 15 August 2016

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