Lossless Multiwavelet Compression of Ultraspectral Sounder Data

Abstract

Multiwavelets are theoretically expected to perform better than traditional wavelets for image compression. Standard and new decomposition methods for multiwavelet compression have been studied in [1], where floating-point multiwavelet transforms are used for lossy image compression. In this paper we investigate both decomposition methods with a reversible integer multiwavelet transform for lossless compression of ultraspectral sounder data. This study shows that the multiwavelet compression method outperforms other well-known methods such as CALIC, SPIHT, and CCSDS IDC for lossless compression on ultraspectral data. Furthermore, the standard multiwavelet decomposition method yields better compression than the new one. We also show that the BiasAdjusted Reordering (BAR) scheme for spectral data preprocessing yields a substantial improvement on multiwavelet compression with the average compression ratio increasing from 2.27 to 2.61.

Orthogonal 4-tap integer multiwavelet transforms using matrix factorization

“…Cheung and Po [6] proposed an integer multiwavelet transform based on box-and-slope multiscaling system and used Haar transform as the associated integer prefilter, which is an approximation to non-trucated transform. Based on the algorithm given in [6], Bormin Huang [7] etc investigated both decomposition methods with a reversible integer multiwavelet transform for lossless compression of ultraspectral sounder data. Patrick J.Van Fleet [8] developed a factorization of 4-tap multiwavelet with multiplicity 2.…”