Abstract
Zerotrees of wavelet coefficients have shown a good adaptability for the compression of three-dimensional images. EZW, the original algorithm using zerotree, shows good performance and was successfully adapted to 3D image compression. This paper focuses on the adaptation of EZW for the compression of hyperspectral images. The subordinate pass is suppressed to remove the necessity to keep the significant pixels in memory. To compensate the loss due to this removal, signed binary digit representations are used to increase the efficiency of zerotrees. Contextual arithmetic coding with very limited contexts is also used. Finally, we show that this simplified version of 3D-EZW performs almost as well as the original one.
Highlights
Since the publication of the Grossmann and Morlet paper [1], theory and applications concerning wavelets have improved
Applications concerned mostly the data analysis field and more precisely in the timescale analysis. Their efficiency to represent complex signals with a limited number of generating functions raised an interest for image coding [4]
3D-embedded zerotree coding of wavelet coefficients (EZW)-nonadjacent form (NAF) is applied to a 256 × 256 × 224 extract of the scene 3 of f970620t01p02 r03 run from AVIRIS sensor on Moffett Field site
Summary
Since the publication of the Grossmann and Morlet paper [1], theory and applications concerning wavelets have improved. Applications concerned mostly the data analysis field and more precisely in the timescale analysis Their efficiency to represent complex signals with a limited number of generating functions raised an interest for image coding [4]. The quasiorthogonal 9/7 wavelet for lossy compression and the 5/3 wavelet for lossless compression with a multiresolution decomposition exhibit good results for a wide range of natural images. This paper looks at the zerotree-based compression techniques and improves them with the use of signed binary representations and arithmetic coding in the context of 3D image encoding. Hyperspectral images can be seen as three-dimensional data where two dimensions correspond to the spatial scene observed and the third dimension to the light spectrum for the pixel. All the details concerning the measures: distortion, bit rate are given later in Section 4, but all are common
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