Wavelet lossless compression of ultraspectral sounder data

2008

SPIE Proceedings

With its bulky volume, the ultraspectral sounder data might still suffer a few bits of error after channel coding. Therefore it is beneficial to incorporate some mechanism in source coding for error containment. The Tunstall code is a variableto-fixed length code which can reduce the error propagation encountered in fixed-to-variable length codes like Huffman and arithmetic codes. The original Tunstall code uses an exhaustive parse tree where internal nodes extend every symbol in branching. It might result in assignment of precious codewords to less probable parse strings. Based on an infinitely extended parse tree, a modified Tunstall code is proposed which grows an optimal non-exhaustive parse tree by assigning the complete codewords only to top probability nodes in the infinite tree. Comparison will be made among the original exhaustive Tunstall code, our modified non-exhaustive Tunstall code, the CCSDS Rice code, and JPEG-2000 in terms of compression ratio and percent error rate using the ultraspectral sounder data.

Section: Resultsmentioning

confidence: 99%

Ultraspectral sounder data compression using the non-exhaustive Tunstall coding

Wei

2008

SPIE Proceedings

“…For comparison the results from JPEG2000 13,14 , JPEG-LS 15 , and CCSDS IDC 5/3 16,17 are listed for comparison 21 . The bit rate of the ICA method compared with JPEG-LS, JPEG2000, CCSDS-IDC 5/3 on the ten AIRS ultrapsectral granules are listed in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Use of independent component analysis for lossless compression of ultraspectral sounder data

Wei

2007

SPIE Proceedings

Independent component analysis has been known for its success in blind source separation. It features a decorrelation capability beyond second-order moments. Recently ICA has been used in lossy compression for target detection in hyperspectral imager data where loss of unimportant features does not affect the detection result. For the ultraspectral sounder data, it is better to be lossless compressed for the ill-posed retrieval of geophysical parameters. In this paper we will try to use ICA in the lossless compression of ultraspectral sounder data. The compression result shows that ICA compares favorably with JPEG2000, JPEG-LS, and CCSDS IDC 5/3 on the ten standard AIRS granules dataset.

“…The multiwavelet compression results are compared with CALIC and wavelet compression methods such as SPIHT, CCSDS IDC 9/7M, and IDC 5/3, reported in [4], for compression of ultraspectral sounder data. Table 2 presents the lossless compression results for the 10 granules.…”

Section: Integer Multiwavelet Compressionmentioning

confidence: 99%

“…Wavelet coding techniques such as SPIHT and CCSDS IDC have been previously studied for lossless compression of ultraspectral sounder data [4]. Multiwavelets are theoretically expected to outperform traditional wavelets for image compression applications due to extra freedom in the design of multiwavelet filters [1]; however, previous experiments [1] for lossy image compression have yielded mixed results.…”

Section: Introductionmentioning

confidence: 99%

Lossless Multiwavelet Compression of Ultraspectral Sounder Data

Sriraja

2006 IEEE International Symposium on Geoscience and Remote Sensing

et al. 2006

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.