Low-complexity and error-resilient hyperspectral image compression based on distributed source coding

In this paper we propose a lossless compression algorithm for hyperspectral images based on distributed source coding; this algorithm represents a significant improvement over our prior work on the same topic, and has been developed during a project funded by ESA-ESTEC. In particular, the algorithm achieves good compression performance with very low complexity; moreover, it also features a very good degree of error resilience. These features are obtained taking inspiration from distributed source coding, and particularly employing coset codes and CRC-based decoding. As the CRC can be used to decode blocks using a reference different from that used to compress the image, this yields error resilience. In particular, if a block is lost, decoding using the closest collocated block in the second previous band is successful about 70% of the times.

[1]  Nasir D. Memon,et al.  Context-based, adaptive, lossless image coding , 1997, IEEE Trans. Commun..

[2]  Enrico Magli,et al.  Low-complexity lossless compression of hyperspectral images using scalar coset codes , 2007, PCS 2007.

[3]  Enrico Magli,et al.  Transform Coding Techniques for Lossy Hyperspectral Data Compression , 2007, IEEE Transactions on Geoscience and Remote Sensing.

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

[5]  Zixiang Xiong,et al.  Distributed source coding for sensor networks , 2004, IEEE Signal Processing Magazine.

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

[7]  Enrico Magli,et al.  Distributed Arithmetic Coding , 2007, IEEE Communications Letters.

[8]  Kannan Ramchandran,et al.  PRISM: a "reversed" multimedia coding paradigm , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[9]  Enrico Magli,et al.  Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images , 2007, EURASIP J. Adv. Signal Process..

[10]  Ying Zhao,et al.  Compression of correlated binary sources using turbo codes , 2001, IEEE Communications Letters.

[11]  K. Anderson.  Low-Cost, Radiation-Tolerant, On-Board Processing Solution , 2005, 2005 IEEE Aerospace Conference.

[12]  D.C. Feldmeier,et al.  Fast software implementation of error detection codes , 1995, TNET.

[13]  R. A. McDonald,et al.  Noiseless Coding of Correlated Information Sources , 1973 .

[14]  Ram Zamir,et al.  The rate loss in the Wyner-Ziv problem , 1996, IEEE Trans. Inf. Theory.

[15]  K. Ramchandran,et al.  Distributed source coding using syndromes (DISCUS): design and construction , 1999, Proceedings DCC'99 Data Compression Conference (Cat. No. PR00096).

[16]  Kannan Ramchandran,et al.  Duality between source coding and channel coding and its extension to the side information case , 2003, IEEE Trans. Inf. Theory.

[17]  Zixiang Xiong,et al.  Compression of binary sources with side information at the decoder using LDPC codes , 2002, IEEE Communications Letters.

[18]  Aaron D. Wyner,et al.  The rate-distortion function for source coding with side information at the decoder , 1976, IEEE Trans. Inf. Theory.

[19]  Antonio Ortega,et al.  Scalable predictive coding by nested quantization with layered side information , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[20]  Nasir D. Memon,et al.  Context-based lossless interband compression-extending CALIC , 2000, IEEE Trans. Image Process..