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Proceedings Article

Hyperspectral images lossless compression using the 3D binary EZW algorithm

[+] Author Affiliations
Kai-jen Cheng, Jeffrey Dill

Ohio Univ. (United States)

Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 865515 (February 19, 2013); doi:10.1117/12.2002820
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From Conference Volume 8655

  • Image Processing: Algorithms and Systems XI
  • Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev
  • Burlingame, California, USA | February 03, 2013

abstract

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. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Kai-jen Cheng and Jeffrey Dill
" Hyperspectral images lossless compression using the 3D binary EZW algorithm ", Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 865515 (February 19, 2013); doi:10.1117/12.2002820; http://dx.doi.org/10.1117/12.2002820


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