Gamboled Image Compression Technique for Low Power

DOI 10.4010/2016.1596
ISSN 2321 3361 © 2016 IJESC
Research Article
Volume 6 Issue No. 6
Gamboled Image Compression Technique for Low Power Devices
with Binary DCT and Torus Automorphism
G.Suseela1, Asnath Victy Phamila.Y2
School of Computing Science and Engineering, VIT University, Chennai Campus, India
[email protected], [email protected]
Abstract:
Image data are huge in size, requiring more storage space and processing power. Low power communication devices like handheld
electronic gadgets, sensor nodes in the sensor network, etc., have to spend more resource for processing the image data. Image
compression is obligatory for resource efficient communication and image retrieval. The standard image compression algorithms are
encompassing complex computations and are not power efficient. Image data are sensitive against attacks and self-descriptive.
Secured image transmission is desirable along with compression for defending against attacks and privacy fortification. Hence forth a
low complex power efficient robust image compression algorithm is focused. This paper presents a new robust image compression
algorithm using Torus-automorphism and Binary DCT.
Keywords: DCT, Image Compression, Torus-automorphism
I.INTRODUCTION
The modern advancements in multimedia based communication
technology and micro electro mechanical systems, have
fostered image and video communication over the resource
restricted low power communication devices like handheld
electronic gadgets, sensor nodes. Image data contains large
amount of pixel redundancies with the neighboring pixels.
Uncompressed raw image data will be requiring more space
and bandwidth, moreover the network resources will be
demoralized. To have efficient storage and transmission
compression is very much required. The process of removing
the redundancies and irrelevancies is termed to be compression.
The main objective any image compression technique is to
remove the redundancies present in the image and to preserve
the significant information present in the image data.
Compression can be lossy compression and lossless
compression. Lossy compression will have greater compression
ratio than lossless techniques with satisfactory image quality
[1]. For battery power supplied low power devices like mobile
phones and camera equipped image sensor nodes has to spend
their energy economically for having extended life span.
Further they can be transform based or non-transform based.
Transform based compression algorithm follows a three step
chained process i.e., (Transformation-Quantization-Entropy
coding).
Transforms will transfigure and group the pixel matrix
based on redundancy and relevancy. The widely used
Transforms are Discrete Cosine Transform (DCT) [2] and
Discrete Wavelet Transform (DWT) [3].For secured image
transmission, image encryption using Torus Automorphism
(TA) is accomplished [4]. Since images are self-descriptive,
will not require any distinctive mechanism to interpret if
tampered. Any image encryption algorithm aims in encrypting
the image into an unreadable ciphered image. TA is basically a
dynamic system whose state changes with time. In image TA is
used to transposition the pixels inside image.
International Journal of Engineering Science and Computing, June 2016
II.PROPOSED SYSTEM
The proposed system in this communication follows three
processes.1.Pixel removal, 2.Torus Automorphism and
3.Binary DCT
Figure 1.Three step Image compression chain
PIXEL BLOCK REMOVAL
The first process is redundancy removal at spatial domain. As
image contains huge amount of redundancy due to neighboring
pixel correlation, this type of redundancy removal is aimed and
removed. This is emphasized in two stages. At first the image is
segmented into tiny matrices each of size 2 x 2 and in second
step alternating blocks are gamboled .In other words odd
numbered blocks are retained and even numbered blocks are
discarded both row and column wise. By doing this the image
size is halved in 2D i.e, 75% of redundancy removed.
TORUS AUTOMORPHISM
Torus Automorphism (TA) is a dynamic system whose state ‘s’
changes with respect to time ‘t’. TA can be viewed as
permutation in two dimensional spaces like matrices using a
transform function [1]. With pixel matrices TA is used for pixel
position trundling within the image in image encryption system.
The new pixel positions are determined using the transform
function as shown in (1). Where xn and yn are the shuffled new
pixel positions and x0, y0 are the original pixel positions. The
result of the TA is an obfuscated image so that the image
becomes unreadable.
.
6655
(1)
http://ijesc.org/
BINARY DCT
Discrete Cosine Transform (DCT) is very much suitable for
resource restricted environment, as it is applied to small blocks
of the pixel matrix usually of size 8x8 [3].The lead role in the
JPEG image compression standard was played by DCT. Even
though DCT matrix requires less memory it involves intensive
complex computation with floating point operations for each
individual block.Trans.D, 2000 introduced a hardware friendly
fast DCT called as Binary DCT [5]. Binary DCT uses only
integer operation and all complex multiplications are replaced
by binary bit shifters. Binary DCT does not
involve any
multiplication operation. It uses only bit shifters and adders.
Each value in the Binary DCT coefficient matrix is an
approximated value of the true DCT matrix. Hence Binary
DCT holds all the properties of DCT like coding gain, energy
compaction, and null DC leakage. Because of the above stated
merits of Binary DCT, the proposed system adapted binary
DCT to make it suitable for resource (processing hardware)
limited environments.
III.EXPERIMENTAL RESULTS AND DISCUSSION
The proposed compression technique has been implemented
and carried out in Matlab 2013a.The experiments have been
repeated for standard gray scale test images of Lena,, peppers
,Baboon and Barbara all of size 512 x512 and results were
presented in Figure 1. The compression performance metric is
determined by the quality of the reconstructed image at
decompressor and compression ratio. One of the widely
accepted models are MSE and PSNR.
Peak Signal to Noise Ratio: PSNR is defined as the degree of
error relative to peak value of the image and the amount of
distortion present in compressed image. PSNR is measured in
decibels and higher the PSNR value higher the image quality
(for 8-bit grey scale image peak pixel value is 255).
.
(2)
Where MSE is Mean Square Error defined amount of existing
between original and reconstructed images.
(3)
Compression Ratio (CR): Higher the compression ratio better
the compression technique. It is defined as ratio between the
sizes of original uncompressed image and the compressed
image.
.
(4)
The
proposed compression technique offers a very good
compression ratio of 1:4. The PSNR and compression ratio
achieved by the proposed system in Table 1.
Input Lena Image
Encrypted And Compressed Lena Image
Decrypted And Decompressed Lena Image
Input peppers Image
Encrypted and compressed peppers Image
Decrypted and decompressed peppers Image
Input Baboon Image
Encrypted and compressed Baboon Image
Decrypted and decompressed Baboon Image
Input Barbara Image
Encrypted and compressed Barbara Image
Decrypted and decompressed Barbara Image
Figure 2. Experimented original, encrypted and decrypted and compressed images
International Journal of Engineering Science and Computing, June 2016
6656
http://ijesc.org/
Table 1. PSNR and CR values
Images
PSNR
CR
Lena
59.00
4
Baboon
48.38
4
Girlface
47.39
4
Peppers
34.57
4
Barbara
42.03
4
IV.CONCLUSION
In this paper, an image compression algorithm is proposed
along with encryption .Experiments have been carried out and
results were analyzed. As far as wireless multimedia
communication is concerned image compression algorithm
with high compression ratio is extremely required. The
proposed system offered compression ratio of 1:4 with
acceptable image quality. Also it emphasized defending
mechanism against various attacks. Since the system used
multiplier less DCT version with integer operation makes it
suitable for low power small hand devices like mobile phones,
tabs and tiny camera sensor nodes in the sensor network.
V.REFERENCES
[1] Kumar G, Shrivastava P, Scholar PG. A Survey of
Various Image Compression Techniques for RGB
Images. International Journal of Engineering Science.
2016 May;4905.
[2] Rao, K., Yip., P.,:’ Discrete Cosine TransformAlgorithms, Advantages, Applications’, Academic.
NewYork,1990
[3] Mohsen Nasri, Abdelhamid Helali, HalimSghaier,
Hassen Maaref,"Adaptive image compression
technique for wireless sensor networks": Computers
& Electrical Engineering. September 2011.
[4] Cristian Duran Faundez, Vincent Lecuire, Francis
Lepage “Tiny block-size coding for energy-efficient
image compression and communication in wireless
camera sensor network”: Signal Processing: Image
Communication 26 (2011).
[5] Tran, T.: ’The binDCT: fast multiplierless
approximation of the DCT’, IEEE Signal Processing
Letters,2000,7,(6),pp.141-144
International Journal of Engineering Science and Computing, June 2016
6657
http://ijesc.org/