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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.

Space multiband charge coupled devices (CCD) camera is now heading for a high spatial resolution, high spectral resolution, high radiation resolution, large field of view, and wide coverage development. All these result in the number of CCD mosaic growing, read-out rate increasing, quantization bits of AD converter increasing, and average shooting time increasing, so that the amount of digitization image data increases sharply. However, the highest data transmission rates of on-board downlink channel are limited. In addition, the amount of flash memory based SSR used on the satellite is also limited. So, it is necessary to compress the on-board CCD images as well.

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. Reference [

The statistics result of on-board image compression.

The transform-based approach usually has an image transform stage, such as discrete cosine transform (DCT), discrete wavelet transform (DWT) [

The prediction-based approaches are widely used by 3D image (like multispectral, hyperspectral image) compression. For now, to cover 1D, 2D, and 3D, coefficients prediction algorithms include hundreds of predictors. For the on-board application, the main prediction methods were DPCM, adaptive DPCM, CCSDS-LDC, CCSDS-MHD, JPEG-LS, and LUT [

Recently, two low-complexity compression approaches are already appearing which are distributed source coding (DSC) [

For now, Peyré and Mallat [

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 redundancy, spectral redundancy, and bit information redundancy.

The latter part of this paper is organized as follows. In Section

To weigh the computational complexity and compression performance, in this paper, we proposed a low-complexity compression algorithm based on deep coupling algorithm for multiband CCD images. We integrate posttransform in wavelet domain, compressive sensing, and distributed source coding in a deeply coupled manner to remove the spatial redundant, spectral redundant, and bit information redundancy.

Figure

Proposed deep coupling coding architecture for multiband CCD images.

In decoding, the check bits and side information are combined in a new code word to correct errors. So the result measurements of

Figure

Proposed deep coupling coding between PT and CS.

In this paper, to obtain a low-complexity yet an efficient posttransform, we use a very simple dictionary which is composed of the Hadamard basis and DCT basis; that is,

Under the low bit rate, the best posttransform basis selection can be expressed as

Under the high bit rate, the best posttransform basis selection can be expressed as

Given the different subbands which include varying degrees of image information, the CS sensing matrices have different sizes. Four CS sensing matrices are denoted as

CS sensing matrices’ size is determined by bit rate. Since the best transform select

The base used in dictionary selection is determined by CS result measurements. In result measurements, we use a prediction approach to compute the amount of information of CS result measurements. The prediction can be expressed as

Figure

Proposed deep coupling coding among PT, CS, and DSC.

The subset of the bands

To ensure that the

Definitions of context windows: (a) and (b) two previous bands, (c) current band.

The theoretical limit of Slepian-Wolf bit rate is

As usually done in the literature, we use the Quickbird multiband remote sensing images. Each group remote sensing images are used to evaluate our deep coupling compression scheme. The size of each group is

Encoding multiband images at 2.0 bpp.

Original band 1

Original band 2

Original band 3

Original band 4

Reconstructed band 1

Reconstructed band 2

Reconstructed band 3

Reconstructed band 4

To objectively evaluate the performance of proposed deep coupling-based compression scheme, extensive experiments were carried out on a number of multispectral data at various coding bit rates. In the first experimental part, we compare the compression results obtained with the proposed coder against those achieved with AT-3DSPIHT [

The test result of multiband images.

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.

The authors declare that there is no conflict of interests regarding the publication of this paper.

The project is sponsored by the China Postdoctoral Science Foundation (no. 2014M550720), the National High Technology Research and Development Program of China (863 Program) (no. 2012AA121503, no. 2012AA121603), and China NSF Projects (no. 61377012, no. 60807004).