A novel method for lossless compression of arbitrarily shaped regions of interest in hyperspectral imagery

We propose a novel algorithm for lossless compression of regions of interest (ROI) in hyperspectral images. The algorithm can compress arbitrarily shaped ROIs as specified by a binary map. The algorithm separates the boundary pixels from the full-context pixels within the ROI and applies Golomb-Rice encoders with different parameters on the boundary and full-context ROI pixels respectively. Experimental results show that the proposed algorithm provides larger compression than JPL's low-complexity hyperspectral image compressing method when applied on individual ROI's.

[1]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Arto Kaarna,et al.  Lossless hyperspectral image compression via linear prediction , 2002, SPIE Defense + Commercial Sensing.

[3]  D. Keymeulen,et al.  GPU lossless hyperspectral data compression system for space applications , 2012, 2012 IEEE Aerospace Conference.

[4]  Solomon W. Golomb,et al.  Run-length encodings (Corresp.) , 1966, IEEE Trans. Inf. Theory.

[5]  Touradj Ebrahimi,et al.  The JPEG 2000 still image compression standard , 2001, IEEE Signal Process. Mag..

[6]  J. Mielikainen,et al.  Lossless compression of hyperspectral images using lookup tables , 2006, IEEE Signal Processing Letters.

[7]  Shuang Wang,et al.  Shape-Adaptive Reversible Integer Lapped Transform for Lossy-to-Lossless ROI Coding of Remote Sensing Two-Dimensional Images , 2011, IEEE Geoscience and Remote Sensing Letters.

[8]  Bormin Huang,et al.  Lossless Compression of Hyperspectral Images Using Clustered Linear Prediction With Adaptive Prediction Length , 2012, IEEE Geoscience and Remote Sensing Letters.

[9]  B. Widrow,et al.  Adaptive noise cancelling: Principles and applications , 1975 .

[10]  Guillermo Sapiro,et al.  The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS , 2000, IEEE Trans. Image Process..

[11]  Matthew A. Klimesh,et al.  Low-complexity lossless compression of hyperspectral imagery via adaptive filtering , 2005 .

[12]  Shipeng Li,et al.  Shape-adaptive discrete wavelet transforms for arbitrarily shaped visual object coding , 2000, IEEE Trans. Circuits Syst. Video Technol..

[13]  Giovanni Motta,et al.  Low-complexity lossless compression of hyperspectral imagery via linear prediction , 2005, IEEE Signal Processing Letters.

[14]  Lenan Wu,et al.  Enhanced DPCM using LMS predictor and composite source model , 1997 .

[15]  S. T. Alexander,et al.  Image compression results using the LMS adaptive algorithm , 1985, IEEE Trans. Acoust. Speech Signal Process..

[16]  I. Maglogiannis,et al.  Region of Interest Coding Techniques for Medical Image Compression , 2007, IEEE Engineering in Medicine and Biology Magazine.