Multispectral Image Compression Based on DSC Combined with CCSDS-IDC

Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches.

[1]  Dong-Wook Kim,et al.  VLSI Architecture of Line-Based Lifting Wavelet Transform for Motion JPEG2000 , 2007, IEEE Journal of Solid-State Circuits.

[2]  Ulug Bayazit.  Adaptive Spectral Transform for Wavelet-Based Color Image Compression , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[3]  Enrico Magli,et al.  A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[4]  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.

[5]  Manoj K. Arora,et al.  Comparative performance of fractal based and conventional methods for dimensionality reduction of hyperspectral data , 2014 .

[6]  Joan Serra-Sagristà,et al.  Extending the CCSDS Recommendation for Image Data Compression for Remote Sensing Scenarios , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Bo-Cai Gao,et al.  Portable Remote Imaging Spectrometer coastal ocean sensor: design, characteristics, and first flight results. , 2014, Applied optics.

[8]  O. Chitsobhuk,et al.  Efficient pass-pipelined VLSI architecture for context modeling of JPEG2000 , 2007, 2007 Asia-Pacific Conference on Communications.

[9]  Hyun Jung Cho,et al.  A performance evaluation on DCT and wavelet-based compression methods for remote sensing images based on image content , 2009, 2009 17th International Conference on Geoinformatics.

[10]  Maryam Imani,et al.  Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples , 2014, IEEE Geoscience and Remote Sensing Letters.

[11]  Zhihua Wang,et al.  A VLSI architecture of JPEG2000 encoder , 2004, IEEE Journal of Solid-State Circuits.

[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]  William A. Pearlman,et al.  Successive Approximation Wavelet Coding of AVIRIS Hyperspectral Images , 2011, IEEE Journal of Selected Topics in Signal Processing.

[14]  Ting Zhang,et al.  JPEG2000-Based Optimization Algorithm for Effective Compression Display of Remote Sensing Images , 2013 .

[15]  Chia-Hung Chang,et al.  High-performance computing in remote sensing image compression , 2011, Remote Sensing.

[16]  Xiaodong Gu,et al.  High speed and bi-mode image compression core for onboard space application , 2010, International Conference on Space Information Technology.

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

[18]  Hongyi Chen,et al.  A VLSI architecture of JPEG2000 encoder , 2004 .

[19]  Huijie Zhao,et al.  Development of a dual-path system for band-to-band registration of an acousto-optic tunable filter-based imaging spectrometer. , 2013, Optics letters.

[20]  Yongfei Zhang,et al.  Visual Distortion Sensitivity Modeling for Spatially Adaptive Quantization in Remote Sensing Image Compression , 2014, IEEE Geoscience and Remote Sensing Letters.

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

[22]  Xuelong Li,et al.  Spectral-Spatial Constraint Hyperspectral Image Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.