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Lossless compression of multispectral images using spectral information
Ma L(马龙); Shi ZL(史泽林); Tang XS(唐旭晟)
Conference NameMIPPR 2009: Multispectral Image Acquisition and Processing
Conference DateOctober 30 - November 1, 2009
Conference PlaceYichang, China
Author of SourceSPIE
Source PublicationProc. SPIE
Publication PlaceBELLINGHAM
Indexed ByEI
EI Accession number20095012547212
Contribution Rank1
KeywordLossless Compression Image Compression Spectral Linear Prediction
AbstractMultispectral images are available for different purposes due to developments in spectral imaging systems. The sizes of multispectral images are enormous. Thus transmission and storage of these volumes of data require huge time and memory resources. That is why compression algorithms must be developed. A salient property of multispectral images is that strong spectral correlation exists throughout almost all bands. This fact is successfully used to predict each band based on the previous bands. We propose to use spectral linear prediction and entropy coding with context modeling for encoding multispectral images. Linear prediction predicts the value for the next sample and computes the difference between predicted value and the original value. This difference is usually small, so it can be encoded with less its than the original value. The technique implies prediction of each image band by involving number of bands along the image spectra. Each pixel is predicted using information provided by pixels in the previous bands in the same spatial position. As done in the JPEG-LS, the proposed coder also represents the mapped residuals by using an adaptive Golomb-Rice code with context modeling. This residual coding is context adaptive, where the context used for the current sample is identified by a context quantization function of the three gradients. Then, context-dependent Golomb-Rice code and bias parameters are estimated sample by sample. The proposed scheme was compared with three algorithms applied to the lossless compression of multispectral images, namely JPEG-LS, Rice coding, and JPEG2000. Simulation tests performed on AVIRIS images have demonstrated that the proposed compression scheme is suitable for multispectral images.
Document Type会议论文
Corresponding AuthorMa L(马龙)
AffiliationShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning Province, 110016, China
Recommended Citation
GB/T 7714
Ma L,Shi ZL,Tang XS. Lossless compression of multispectral images using spectral information[C]//SPIE. BELLINGHAM:SPIE,2009:749416.
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