Low-Complexity Compression Method for Hyper spectral Images Based on Distributed Source Coding

ABSTRACT:
In this letter, we propose a low-complexity discrete cosine transform (DCT)-based distributed source coding (DSC) scheme for hyperspectral images. First, the DCT was applied to the hyperspectral images. Then, set-partitioning-based approach was utilized to reorganize DCT coefficients into waveletlike tree structure and extract the sign, refinement, and significance bitplanes. Third, low-density parity-check-based Slepian–Wolf (SW) coder was adopted to implement the DSC strategy. Finally, an auxiliary reconstruction method was employed to improve the reconstruction quality. Experimental results on Airborne Visible/Infrared Imaging Spectrometer data set show that the proposed paradigm significantly outperforms the DSC-based coder in wavelet transform domain (set partitioning in hierarchical tree with SW coding), and its performance is comparable to that of the DSC scheme based on informed quantization at low bit rate.

EXISTING SYSTEM:

Zhang et al. propose a lossless compression method for multispectral images based on DSC and prediction with very low encoder complexity. Cheung proposes the DSC-based lossy way in wavelet transform (WT) domain, named as set partitioning in hierarchical tree with Slepian–Wolf coding (SW-SPIHT). Magli et al. puts forth lossless and lossy DSC-based compression methods. Moreover, they demonstrate that the presented DSCbased compression frameworks are very promising.

The algorithm presented divides the images into several slices and employs the adaptive region-based predictor to capture spatially varying spectral correlation, bringing in lossless compression and progressive coding. It achieves good lossless compression performance through complex predictions and Markov random field, while our algorithm uses DCT and SPIHT to implement lossy compression. Furthermore, the methods in existing are for multispectral images, of which the spectral resolution is much lower and the statistical properties are more complex and non-stationary than hyper-spectral images.

DISADVANTAGES OF EXISTING SYSTEM:

HYPERSPECTRAL image compression requires lowcomplexity encoder because it is usually completed on board where the energy and memory are limited. However, the measures with traditional encoders are not simple enough. For example, many algorithms, including those based on JPEG2000 and 3-D transform, have excessive complexity.

PROPOSED SYSTEM:
In this letter, we put forward a low-complexity DSC scheme for onboard compression of hyperspectral images. In particular, our method is conducted in discrete cosine transform (DCT) domain, rather than WT domain. We modify Xiong’s embedded zerotree DCT (EZDCT) to extract biplanes of the reorganized DCT coefficients into waveletlike tree structure, yielding significance, sign, and refinement biplanes. The auxiliary reconstruction is applied to improve the reconstruction quality at the decoder. According to the characteristics of DSC, we make further use of the side information to reconstruct DCT coefficients, reducing the quantization errors. Furthermore, we use the Gray code for the refinement bits of DCT coefficients which can significantly and efficiently improve the correlation between the source and the side information.

ADVANTAGES OF PROPOSED SYSTEM:

ü Very competitive, compared with other DSC-based coding methods for hyper spectral images.

ü The low-complexity DSC-based scheme with auxiliary reconstruction is feasible for hyper spectral image compression.


MODULES:

1.     Import hyper-spectral images
2.     Analyzing Image
3.     Low Complexity Discrete Cosine Transform (DCT)
4.     Compress to image.
5.     Rate–Distortion Comparison

MODULES DESCRIPTION:

Import hyper-spectral images
User can import images of any type into the project. Most probably supported image Format is JPEG and BMP.  Image container holds the image.

Analyzing Image
Image can be zoomed by the user in the ratio of 16*16 pixel rate. Also the image blocks are classified. Every single pixel value listed out. Yuv format classification of image is possible.

Low Complexity Discrete Cosine Transform (DCT)
HYPERSPECTRAL image compression requires lowcomplexity encoder because it is usually completed on board where the energy and memory are limited. However, the measures with traditional encoders are not simple enough. For example, many algorithms, including those based on JPEG2000 and 3-D transform, have excessive complexity. Given the close correlation among hyperspectral images, we can employ distributed source coding (DSC) principle to compress them efficiently at a lower cost. Compared with conventional source coding schemes, the DSC method can shift the complexity from encoder to decoder.

Compress to image
Image Writing Classes are used to make the image after applying the Fuzzy Filter. Here image pixels are verified with original to make some of the quality regarding adjustments. So that the quality of image absolutely preserved after reduction

Rate–Distortion Comparison
The proposed system is implemented, and the performance is evaluated on senses sc0 and sc3 of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; Yellowstone). AVIRIS is a spectrometer with 224 bands, and the size of this raw image is 512 lines and 680 pixels.We divide the images into four groups. The code rate of the key bands is 2 bpp. In this experiment, we use eight as the size of DCT kernel and three as the level of WT domain correspondingly. LDPCA code with the length of 396 specified and is applied, which can perform within 10% of the SWbound at moderate rate.

HARDWARE REQUIREMENTS

•                     SYSTEM             : Pentium IV 2.4 GHz
•                     HARD DISK        : 40 GB
•                     FLOPPY DRIVE  : 1.44 MB
•                     MONITOR           : 15 VGA colour
•                     MOUSE               : Logitech.
•                     RAM                    : 256 MB
•                     KEYBOARD       : 110 keys enhanced.



SOFTWARE REQUIREMENTS

•                     Operating system           :-  Windows XP Professional
•                     Front End             :-  Microsoft Visual Studio .Net 2008
•                     Coding Language : - C# .NET.

REFERENCE:

Xuzhou Pan, Rongke Liu, Member, IEEE, and Xiaoqian Lv, “Hyperspectral Images Based on Distributed Source Coding”, IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 9, NO. 2, MARCH 2012.