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.
