2 May 2008 Improving the performance of PCA and JPEG2000 for hyperspectral image compression
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In our previous paper, it has been demonstrated that principal component analysis (PCA) can outperform discrete wavelet transform (DWT) in spectral coding for hyperspectral image compression and a superior rate distortion performance can be provided in conjunction with 2-dimensional (2D) spatial coding using JPEG2000. The resulting compression algorithm is denoted as PCA+JPEG2000. In this paper, we further investigate how the data size (i.e., spatial and spectral size) influences the performance of PCA+JPEG2000 and provide a rule of thumb for PCA+JPEG2000 to perform appropriately. We will also show that using a subset of principal components (PCs) (the resulting algorithm is denoted as SubPCA+JPEG2000) can always yield a better rate distortion performance than PCA+JPEG2000 with all the PCs being preserved for compression.
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Qian Du, Qian Du, Wei Zhu, Wei Zhu, } "Improving the performance of PCA and JPEG2000 for hyperspectral image compression", Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69661I (2 May 2008); doi: 10.1117/12.777317; https://doi.org/10.1117/12.777317

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