In this paper, a lossless to lossy transform based image compression of hyperspectral images based on Integer Karhunen-Loève Transform (IKLT) and Integer Discrete Wavelet Transform (IDWT) is proposed. Integer transforms are used to accomplish reversibility. The IKLT is used as a spectral decorrelator and the 2D-IDWT is used as a spatial decorrelator. The three-dimensional Binary Embedded Zerotree Wavelet (3D-BEZW) algorithm efficiently encodes hyperspectral volumetric image by implementing progressive bitplane coding. The signs and magnitudes of transform coefficients are encoded separately. Lossy and lossless compressions of signs are implemented by conventional EZW algorithm and arithmetic coding respectively. The efficient 3D-BEZW algorithm is applied to code magnitudes. Further compression can be achieved using arithmetic coding. The lossless and lossy compression performance is compared with other state of the art predictive and transform based image compression methods on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. Results show that the 3D-BEZW performance is comparable to predictive algorithms. However, its computational cost is comparable to transform- based algorithms.
This paper presents a transform based lossless compression for hyperspectral images which is inspired by Shapiro
(1993)’s EZW algorithm. The proposed compression method uses a hybrid transform which includes an integer
Karhunrn-Loeve transform (KLT) and integer discrete wavelet transform (DWT). The integer KLT is employed to
eliminate the presence of correlations among the bands of the hyperspectral image. The integer 2D discrete wavelet
transform (DWT) is applied to eliminate the correlations in the spatial dimensions and produce wavelet coefficients.
These coefficients are then coded by a proposed binary EZW algorithm. The binary EZW eliminates the subordinate pass
of conventional EZW by coding residual values, and produces binary sequences. The binary EZW algorithm combines
the merits of well-known EZW and SPIHT algorithms, and it is computationally simpler for lossless compression. The
proposed method was applied to AVIRIS images and compared to other state-of-the-art image compression techniques.
The results show that the proposed lossless image compression is more efficient and it also has higher compression ratio
than other algorithms.