In this paper, a hyperspectral image lossy coder using three-dimensional Embedded ZeroBlock Coding (3D EZBC)
algorithm based on Karhunen-Loève transform (KLT) and wavelet transform (WT) is proposed. This coding scheme
adopts 1D KLT as spectral decorrelator and 2D WT as spatial decorrelator. Furthermore, the computational complexity
and the coding performance of the low-complexity KLT are compared and evaluated. In comparison with several stateof-
the-art coding algorithms, experimental results indicate that our coder can achieve better lossy compression
Motion-compensated three-dimensional embedded zeroblock coding (MC 3-D EZBC) is a successful state-of-the-art video compression algorithm. We propose a hyperspectral image compression coder based on the 3-D EZBC algorithm without motion compensation. This coder adopts the 3-D wavelet transform to decorrelate and the 3-D EZBC algorithm without motion compensation to process bitplane zeroblock coding. For achieving good coding performance, the diverse 3-D wavelet transform structures and the several wavelet filters are respectively compared and evaluated on the basis of floating-point lossy compression and lossless-to-lossy compression. We also study the problems of the optimal unitary scaling factors and list initialization order. Finally, the best choices were found for a given application, via the extensive experiments and analyses. Moreover, in comparison with several state-of-the-art wavelet coding algorithms, 3-D EZBC can provide better compression performance and unsupervised classification accuracy. Experimental results show that the average lossy compression performance (in floating-point mode and integer-based mode) of our coder respectively outperforms 3-D set partitioning in hierarchical trees (SPIHT) by 1.26 dB, 3-D set-partitioned embedded block (SPECK) by 0.68 dB, symmetric-tree (AT) 3-D SPIHT by 0.39 dB, and JPEG 2000-MC by 0.25 dB at 0.1 to 3.0 bits per pixel per band, and the lossless coding performance of 3-D EZBC is about 5% to 7% better than that of 3-D SPECK, 3-D SPIHT, and AT 3-D SPIHT. So the 3-D EZBC algorithm is also a good candidate to compress hyperspectral images.