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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 14 ISSUE 1 – JUNE 2016 - ISSN: 2349 – 9303
27
Implementation Based Compression of Hyper spectral
Images Using Lifting Transform
Mahendran. M 1
Department of ECE,
National Engineering College,
Kovilpatti, India
Jayavathi. S.D2
Department of ECE,
National Engineering College,
Kovilpatti, India
Abstract– Hyperspectral images are a satellite image. It contains a lot of information and set of bands. The HSI compression is an
important issue in remote sensing application. The proposed hyperspectral image compression is based on lifting wavelet transform
(LWT).In this project, the image file is convert into a header file, it contain a pixels value of the image. And then it is followed by 2D
LWT is applied to the wavelet coefficient for hyperspectral image compression. The proposed method implemented on Xilinx 10.1,
Spartan3-EDK kit and tested the hyperspectral image. Experiment on data from the urban data is analyzed. Hyperspectral image
compression is a lossy image compression technique and it provides good computational speed. Finally the compressed output image is
display the visual basic tool.
Index Terms— Compression, hyperspectral images, 2D lifting wavelet transform.
—————————— ——————————
1. INTRODUCTION
yperspectral images are widely used in a variety of fields,
such as target detection, material identification, ground
mapping and agriculture. The advancement of sensor
technology produces remotely sensed data that have a large
number of spectral bands. There is an increasing need for
efficient compression techniques for these hyperspectral images.
The compression of hyperspectral images can be implemented
by detecting the spatial and spectral redundancies. The
compression methods can be classified into two types: lossless
compression and lossy compression. A lossless technique that
decompresses data back to its original from without any loss.
Redundant data is removed from compression and added to
decompression. Lossless compression methods are
recommended in hyperspectral images due to the huge quality of
data and the data loss must be small. Most the lossy compression
methods resort to transform based approaches. In particular
transformed based methods, principal component analysis has
commonly used, often followed by 2-D transform such as the
DWT or DCT. Several methods expand known two dimensional
transform based methods into 3D applications, including SPIHT
(set partitioning in hierarchical trees), SPECK (set partitioning
embedded block). Most lossy compression methods are
developed to minimize mean squared errors between original
and reconstruct the pixels.
In this paper, we propose the image file is converting a
header file, and it is followed by 2D LWT is applied. Finally
implement on FPGA spartan3 and different image result is
analyzed.
2. IMAGE COMPRESSION METHODS
2.1Conversion of image to Header File
Matrix can be used to represent the image, the data type
of matrix is unit 8, and each element of matrix corresponds to a
pixel image, these pixel values are in the interval of [0, 255].
Figure 1 is the example for image to pixels values.
Figure 1 : Block Diagram of Proposed Method
2.2 Proposed Compression Technique
Color space conversion is a converts RGB image to
Grayscale image by eliminating the hue and saturation .
H
INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 14 ISSUE 1 – JUNE 2016 - ISSN: 2349 – 9303
28
Figure 2 : Block Diagram of Proposed Method
C. Wavelet Transform:
The wavelet transform decomposes a signal into a set of
basics function. The wavelet lifting scheme is a method for
decomposing wavelet transform into a set of stages. This method
based on haar wavelet.
The lifting scheme is an alternative method of
computing the wavelet coefficients.
Advantages of the lifting scheme:
Requires less computation time and less memory.
Linear, nonlinear, and adaptive wavelet transform is
feasible, and the resulting transform is invertible and
reversible.
A spatial domain construction of bi-orthogonal wavelets
consists of the 4 operations:
Split: Splitting the signal into two parts is called lazy wavelets,
because we have not performed any mathematical operations. It
divides input data into odd and even function.
𝑆𝐾(0)
= 𝑋2𝑖(0)
; 𝑑𝑘(0)
= 𝑋2𝑖+1(0)
(2)
Predict: The even and odd samples are interleaved. If the signal
is having locally correlated structure, then even and odd samples
are highly correlated. It is very easy to predict odd samples from
even samples.
𝑑𝑘(𝑟)
= 𝑑𝑘(𝑟−1)
− 𝑃𝑗𝑟 𝑆𝑘+𝑗(𝑟−1)
(3)
Update: The coarser signal must have same average value that
of original signal. To do this, we require lifting the λ -1, k with
help ofwavelet coefficients γ-1, k. After lifting process, mean
value of original signal and transformed signal remains same.
We require constructing update operator U for this lifting
process.
𝑟 = 𝑆𝑘 𝑟 − 1 + 𝑈𝑗𝑟 𝑑𝑘+𝑗(𝑟)
(4)
Figure 3: Lifting Scheme
3. HARDWARE DESCRIPTION
Figure 4: TYRO PLUS SPARTAN3 (EDK) Board
The Spartan-3 EDK Board provides a powerful, self-
contained development platform for designs targeting the new
Spartan-3 FPGA from Xilinx. It features a 200k gate Spartan-3,
on-board I/O devise, and 1MB fast asynchronous SRAM,
making it the perfect platform to experiment with any new
design, from a simple logic circuit to an embedded processor
core. The board also contains a platform Flash JTAG-
programmable ROM, so designs can easily be made non-
volatile.
The Spartan-3Starter Board is fully compatible with all
versions of the Xilinx ISE tools, including the free web pack.
The board ships with a power supply, and programming cable.
INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 14 ISSUE 1 – JUNE 2016 - ISSN: 2349 – 9303
29
Figure5: Block Diagram of Xilinx Spartan-3 EDK
Static random-access memory (SRAM) is a type of
semiconductor memory that uses bitable latching circuitry to store
each bit. The SRAM array forms either a single 256Kx32 SRAM
memory or two independent 256Kx16 arrays. Both SRAM
devices share common write-enable, output-enable and address
signals. However, each device has a separate chip select enable
control and individual byte-enable controls to select the high or
low byte in the 16-bit data word.
The 256Kx32 configuration is ideally suited to hold
Micro Blaze instructions. However, it alternately provides high-
density data storage for a variety of applications, such as digital
signal processing (DSP), large data FIFOs, and graphics buffers.
4. EXPERIMENTAL RESULTS
The hyperspectral images compression steps of urban
image use the compression method based on 2D LWT encoding
decoding and are shown in Figure 6(a), the input hyperspectral
image is formed by combining the bands 85, 30, 110 for the
RGB colors respectively. Figure6(b), shows the grayscale image
(255x255) of the urban data, and Figure7 shows the conversion
of input image to header file, that header file (image.h) contains
pixels value of the input image.
(a) (b)
Figure 6: Hyperspectral Images: (a) Input urban image
(b) Grayscale Image
Figure 7: Conversion of input image to header file
INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 14 ISSUE 1 – JUNE 2016 - ISSN: 2349 – 9303
30
Figure 8: Proposed Spartan3 EDK Kit
Figure 8 shows the proposed hardware spartan3 EDK kit.
Next power on the board and the system can be initialized by
update the hardware bit stream. This updated bit stream can then
be downloaded to the FPGA. Under device configuration, choose
download bit stream. The bit stream is initialized with the
executable. The arrows are specifying the program download
successfully.
Figure 9: Snapshot for Xilinx Platform Studio
Figure 10: Snapshot for Launch XMD
Figure 9 shows the snapshot for Xilinx platform studio; Figure
10 shows the launch XMD of the debug steps, first type the CD
filename and ‗dowexecutable.elf‘ in command window. It is
download the program and execute the device configuration,
finally run the program.
Figure 11: lifting wavelet transforms (2D)
Figure 11 shows lifting wavelet transform of the output
image for cameraman. Figure 12 shows the LWT first
decomposition of the output image for hyperspectral image.
Figure 12: 1D Lifting wavelet transform for an hyperspectral
image
The performance of the compression techniques is
analyzed and compared in terms of computational time and
compression ratio.
CR = 𝒏𝒐.𝒐𝒇 𝒃𝒊𝒕𝒔 𝒊𝒏 𝒐𝒓𝒈𝒊𝒏𝒂𝒍 𝒊𝒎𝒂𝒈𝒆
𝒏𝒐 𝒐𝒇 𝒃𝒊𝒕𝒔 𝒊𝒏 𝒄𝒐𝒎𝒑𝒓𝒆𝒔𝒔𝒆𝒅 𝒊𝒎𝒂𝒈𝒆 (5)
INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 14 ISSUE 1 – JUNE 2016 - ISSN: 2349 – 9303
31
5. CONCLUSION An improved method is proposed for the implementation
based compression of hyperspectral images using lifting wavelet
transform. First we applied PCA to the input image for extracting
the features and then it is followed by lifting wavelet transform.
First the image file is convert into a header file, it contain a pixels
value of the image. And then it is followed by 1D LWT is applied
to the wavelet coefficient for hyperspectral image compression. It is
lossy compression technique, it provides good computational
speed. Finally the compressed output image is obtained by taking
inverse lifting wavelet transform to the image. The performance
of the output image is analyzed. In future, the above algorithm will
be implementation based two dimensional wavelet transform on
different size of images.
REFERENCES
[1]. S.Kala, Dr.S.Vasuki, ―FPGA Based Hyperspectral image
Compression Using DWT and DCT‖, Australian journal of
basic and applied sciences, May 2014.
[2]. Chulhee Lee, SungwookYoun, TaeukJeong, Eunjae Lee, and Joan
Serra-Sagristà, ―Hybrid Compression of Hyper spectral Images
Based on PCA with Pre-Encoding Discriminant Information‖,
IEEE geosciences and remote sensing letters, vol. 12, no.
7, July 2015.
[3]. Merav Huber-Lerner, OferHadar, Senior Member, IEEE, Stanley
R. Rotman, Member, IEEE, and Revital Huber-Shalem,
―Compression of Hyper spectral Images Containing a Sub pixel
Target‖, IEEE journal of selected topics in applied earth
observations and remote sensing, vol. 7, no. 6, June
2014 .
[4]. A.AliceBlessie, J. Nalini and S.C.Ramesh, ―Image Compression
Using Wavelet Transform Based on the Lifting Scheme and its
Implementation‖, IJCSI International Journal of Computer
Science Issues, Vol. 8, Issue 3, No. 1, May 2011.
[5]. William A. Pearlman, Asad Islam, Nithin Nagaraj, and Amir
Said, ―Efficient, Low-Complexity Image Coding with a Set-
Partitioning Embedded Block Coder‖, Bellingham, WA, USA:
SPIE, Nov. 2013.
[6]. S.Narasimhulu, Dr.T.Ramashri, ―Gray-Scale Image Compression
Using DWT-SPIHT Algorithm‖, IJERA, August 2012.
[7]. Lena Chang, Yang-Lang Chang, Z. S. Tang, and Bormin
Huang,―Group and Region Based Parallel Compression Method
Using Signal Subspace Projection and Band Clustering for Hyper
spectral Imagery‖, IEEE journal of selected topics in applied
earth observations and remote sensing, vol. 4, no. 3,
September 2011.
[8]. Qian Du, and James E. Fowler, ―Hyperspectral Image
Compression Using JPEG2000 and Principal Component
Analysis‖, IEEE geosciences and remote sensing letters,
vol. 4, no. 2, April 2007.
[9]. X. Tang and W. A. Pearlman, ―Three-dimensional wavelet-based
compression of images in Hyperspectral Data Compression‖, G.
Motta, F. Rizzo, and J. A. Storer, Eds. Norwell, MA, USA:
Kluwer, April 2006.
[10].William A. Pearlman, Asad Islam, Nithin Nagaraj, and Amir
Said, ―Efficient, Low- Complexity Image Coding With a Set-
Partitioning Embedded Block Coder IEEE transactions on
circuits and systems for video technology, vol. 14, no.11,
November 2004.
[11].P.L.Dragotti, G. Poggi, and A. R. P. Ragozini, ―Compression of
multispectral images by three-dimensional SPIHT algorithm,‖
IEEE Trans.Geosci. Remote Sens., vol. 38, no. 1, pp. 416–428,
Jan. 2000.