Multiband CCD Image Compression for Space Camera with Large Field of View

Space multiband CCD camera compression encoder requires low-complexity, high-robustness, and high-performance because of its captured images information being very precious and also because it is usually working on the satellite where the resources, such as power, memory, and processing capacity, are limited. However, the traditional compression approaches, such as JPEG2000, 3D transforms, and PCA, have the high-complexity. The Consultative Committee for Space Data Systems-Image Data Compression (CCSDS-IDC) algorithm decreases the average PSNR by 2 dB compared with JPEG2000. In this paper, we proposed a low-complexity compression algorithm based on deep coupling algorithm among posttransform in wavelet domain, compressive sensing, and distributed source coding. In our algorithm, we integrate three low-complexity and high-performance approaches in a deeply coupled manner to remove the spatial redundant, spectral redundant, and bit information redundancy. Experimental results on multiband CCD images show that the proposed algorithm significantly outperforms the traditional approaches.

[1]  B. Das,et al.  Data-folded architecture for running 3-D DWT using 4-tap Daubechies filters , 2005 .

[2]  Valentin Muresan,et al.  An optimal adder-based hardware architecture for the DCT/SA-DCT , 2005, Visual Communications and Image Processing.

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

[4]  Michael W. Marcellin,et al.  JPEG2000 - image compression fundamentals, standards and practice , 2013, The Kluwer international series in engineering and computer science.

[5]  David S. Taubman,et al.  High performance scalable image compression with EBCOT , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[6]  Ian Blanes,et al.  Cost and Scalability Improvements to the Karhunen–Loêve Transform for Remote-Sensing Image Coding , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Martin Sweeting,et al.  Image compression systems on board satellites , 2009 .

[8]  Kyeongcheol Yang,et al.  Quasi-cyclic LDPC codes for fast encoding , 2005, IEEE Transactions on Information Theory.

[9]  Stéphane Mallat,et al.  Discrete bandelets with geometric orthogonal filters , 2005, IEEE International Conference on Image Processing 2005.

[10]  A. Aggoun Compression of 3D Integral Images Using 3D Wavelet Transform , 2011, Journal of Display Technology.

[11]  Feng Wu,et al.  Image representation by compressive sensing for visual sensor networks , 2010, J. Vis. Commun. Image Represent..

[12]  J. M. Shapiro An embedded wavelet hierarchical image coder , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[13]  马彩文 Ma Cai-wen,et al.  A Compression Algorithm of AT-3DSPIHT for LASIS′s Hyperspectral Image , 2010 .

[14]  J M Bramble,et al.  Image data compression. , 1988, Investigative radiology.

[15]  Xavier Delaunay,et al.  Satellite image compression by directional decorrelation of wavelet coefficients , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  Guangming Shi,et al.  Hyperspectral image compression using distributed source coding and 3D SPECK , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[17]  Houqiang Li,et al.  Distributed coding techniques for onboard lossless compression of multispectral images , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[18]  Michael W. Marcellin,et al.  JPEG2000 - image compression fundamentals, standards and practice , 2002, The Kluwer International Series in Engineering and Computer Science.