An approach for color image compression of bmp and tiff images using dct and dwt

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 6, Issue 1, January (2015), pp. 19-26© IAEME
19
AN APPROACH FOR COLOR IMAGE COMPRESSION OF
BMP AND TIFF IMAGES USING DCT AND DWT
A.H.M. Jaffar Iqbal Barbhuiya1
, Tahera Akhtar Laskar2
, K. Hemachandran3
1, 2, 3
Department of Computer Science, Assam University, Silchar, Assam, India
ABSTRACT
Now a days image compression has become is an indispensible part of digitized image
storage and transmission. Compression of an image is necessary before storing and transmitting it
due to its limitation of storage and bandwidth capacity. Wavelet transform decomposes complexion
of images into its elementary forms. In this paper, a comparative study has been carried out on image
compression using DCT (Discrete Cosine Transform) and DWT (Discrete Wavelet Transform). A
comparison is outlined to emphasize the results of this compression system between DCT and DWT
using BMP (Bitmap Image) and TIFF (Tag Index File Format) color images. We have done
conversion of color images into gray scale and also compression of gray scale image is shown after
conversion using DWT method. DWT algorithm performs much better than DCT algorithms in
terms of Mean Square Error (MSE) and Peak Signal to Noise Ratio (PNSR).
Keywords: DCT; DWT ; PSNR; MSE; Wavelet Transform; Image Compression; Huffman Coding;
BMP; TIFF; Compression ratio.
I. INTRODUCTION
An image can be defined as a matrix of pixel or intensity values. Image compression is used to
reduce the redundancy and randomness present in the image because to increase the storing capacity
and efficiency level of the images. Therefore it is essential to compress the images by storing only the
required information needed to reconstruct the image. To compress any image, redundancy must be
removed. Sometimes images having large areas of same color will have large redundancies and
similarly images that have frequent and large changes in color will be less redundant and harder to
compress[1-3].
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 6, Issue 1, January (2015), pp. 19-26
© IAEME: www.iaeme.com/IJCET.asp
Journal Impact Factor (2014): 8.5328 (Calculated by GISI)
www.jifactor.com
IJCET
© I A E M E

20
The main objective of this paper is to reduce irrelevance and redundancy of the JPEG and
PNG image data in order to be able to store or transmit data in an efficient form using DCT and
DWT. We have tried to study the different image compression algorithm and evaluate their
performance on different image formats and also developed a system for image compression using
DWT and compare the results with the existing techniques or systems. Image compression can be
done in two ways:
• Lossy Compression
• Lossless Compression
If any pixel value is changed from a digital image and then energy will be lost and this
technique is called ‘lossy’ compression. The amount of information retained by an image after
compression and decompression is known as ‘lossless’ compression [7].
In this research paper, a modified and competent image compression scheme is proposed
based on DWT and which results a good compression ratio without degrading the quality of the
image. The proposed algorithm has been analyzed and compared with some other existing method.
Several quality measurement techniques like PSNR and MSE have been considered to determine the
image compression with respect to the reference image. For that purpose some traditional images are
taken and have done experiment on it [1-3].
This paper structure contains the following sections: section 2 describes the previous work
done by different authors, section 3 describes the basic principles of image compression techniques
and related works, section 4 illustrates the fundamentals of wavelet transform, section 5 presents the
proposed method using DWT, section 6 demonstrates the experimental results and discussions and
finally section 7 concludes the paper.
II. RELATED WORKS
Image compression has many practical applications because of huge data storage,
transmission and retrieval for medical imaging, documents and videoconferencing. Chowdhury and
Khatun [1] introduced the performance of the image compression algorithm and provide sufficient
high compression ratios compared to other techniques. Sukanya and Preethi [2] has explained about
compression methods such as JPEG 2000,EZW,SPHIT (Set Partition in Hierarchical Trees) and HS-
SPHIT (Highly Scalable-SPHIT) on the basis of hierarchical time ,error comparison, MSE, PSNR
and compression ratio. Qureshi [9] in his Computational Project ‘image compression using wavelet
transform’ described how wavelets can be used in image compression. Lawson and Zhu [12]
introduced DWT as a tutorial form and illustrates how it how it may be used as a part of of an image
coding algorithm. Averbuch et al. [3] has tested several compression techniques and 512X512
images are trained together and common table codes were created to achieve a compression ratio 60-
65 and a PSNR of 30-33 and compression ratio 35-36 and a PSNR of 35-37. Talukdar and Harada
[4] in their paper explained the 2D Discrete Wavelet Transform (DWT) has been applied and the
detail matrices from the information matrix of the image have been estimated. Wavelet transform is
used in the reconstructed image and is amalgamated by the estimated detail matrices and information
matrix. The compression quality of image has been calculated using Compression Ratio (CR), Peak
Signal to Noise Ratio (PSNR), Mean Opinion Score (MOS), and Picture Quality Scale (PQS).
III.IMAGE COMPRESSION TECHNIQUES
The basic encoding method for transform based compression works as follows:

21
1) Image transform: Divide the source image into blocks and apply the transformations to the
blocks.
2) Parameter quantization: The data generated by the transformation are quantized to reduce the
amount of information. This step represents the information within the new domain by reducing the
amount of data. Quantization is in most cases not a reversible operation because of its lossy property.
3) Encoding: Encode the results of the quantization. This last step can be error free by using Run
Length encoding or Huffman coding. It can also be lossy if it optimizes the representation of the
information to further reduce the bit rate.
Transform based compression is one of the most useful applications. Combined with other
compression techniques, this technique allows the efficient transmission, storage, and display of
images that otherwise would be impractical [4].
4) DCT-Based Transform Coding: The DCT was first applied to image compression in the work
by Ahmed et al [5]. DCT is a transform coding method comprising four steps. The source image is
first partitioned into sub-blocks of size 8x8 pixels in dimension. Then each block is transformed
from spatial domain to frequency domain using a 2-D DCT basis function. The resulting frequency
coefficients are quantized and finally output to a lossless entropy coder. DCT is an efficient image
compression method since it can decorrelates pixels in the image and compact most image energy to
a few transformed coefficients. BMP and TIFF may be replaced by wavelet-based image
compression algorithms, which have better compression performance [5].
IV. THE WAVELET TRANSFORM
A wavelet is a mathematical function used to divide a given function or continuous-time
signal into different scale components. It can assign a frequency range to each scale component. All
scale components that matches its scale can be studied with a resolution. It is the delegation of a
function by wavelets.
߰௔,௕ሺ‫ݐ‬ሻ =
ଵ
ඥ|௔|
߰ ቀ
௧ି௕
௔
ቁ , ܽ, ܾ ∈ ܴ, ܽ ≠ 0,
Where ψ is a wavelet function, a, is another function which measure the degree of compression, and
b, is a translation function which measures the time location of the wavelet [8].
A. Wavelet Definition: Wavelets are mathematical functions that cut up data into different
frequency components. The fundamental idea behind the wavelets is to analyze the signal into
different scales and resolutions [2].
B. Quantization: Quantization is the process where actual reduction of image is done. It is a lossy
compression technique which basically used in DCT data quantization and DWT data quantization for
BMP and TIFF images.
C. Entropy Encoding: Entropy encoding is a lossless data compression method. Two commonly
used entropy encoding techniques are Huffman coding and Arithmetic coding.
D. Discrete Wavelet Transform (DWT): Suppose a function f(x) is a continuous function and is
expanded in a sequence of numbers, then the resulting coefficients are called Discrete Wavelet
Transform (DWT) of f(x).

22
The discrete wavelet transform in two dimensions of functions f(x,y) of size M×N is [6]
Wφ ሺj଴, m, nሻ =
1
√MN
෍ ෍ fሺx, yሻφ୨଴,୫,୬
ሺx, yሻ
୒ିଵ
ଢ଼ୀ଴
୑ିଵ
ଡ଼ୀ଴
The DWT can be decomposed into three level of decomposition. For example, decomposition
level 1, decomposition level 2 and decomposition level 3. The above Fig. 1 shows each level of
decomposition. Compression ratio of an image can be calculated using the following formula:
Compression Ratio (%) =Original image size-compressed image size/original image*100
V. PROPOSED COMPRESSION METHOD USING DWT
This portion describes the compression method based on DWT. The basic method of the
wavelet transform is selecting a color input image of any format from the computer and to find out the
size of the image, compressed size of the image, percentage of compression using DCT and
percentage of compression using DWT and then convert the compressed image into gray scale and
finally find the compression ratio of gray scale image and also find out the PSNR and MSE of the
image using DWT and IDWT of three level of decomposition. The method is given below:
Read an input image of any format from the user. We are using 2D wavelet decomposition
with respect to a haar or doubencies wavelet computes the approximation coefficients matrix and
detail coefficient matrixes horizontal, vertical & diagonal respectively which is obtained by wavelet
decomposition of the input matrix. Again using 2D wavelet decomposition with respect to a haar or
doubencies wavelet computes the approximation and detail coefficients which are obtained by
wavelet decomposition of the approximation coefficient matrix. This is considered as level 2.
Again if we apply the haar wavelet transform from approximation coefficient matrix which is
considered as approximation coefficient 1 for level 3. Again we will take inverse transform for level
1, level 2 & level 3 that is, image input and approximation coefficients respectively for each level of
decomposition. We can reconstruct the images for level 1, level 2 & level 3. After this whole process
it will display the results of reconstruction 1, reconstruction 2 and reconstruction 3 that is, level 1, 2
and 3 with respect to the original image. The Fig. 2 shows the basic compression method of our work.
LL HL
LH HH
1st
Level
LL HL HL
LH HH
LH HH
2nd
Level
LL HL HL HL
LH HH
LH HH
LH HH
3rd
Level
Figure 1. Wavelet Decomposition

23
VI. RESULTS AND DISCUSSIONS
In this research, an efficient compression method based on DWT is proposed. The algorithm has
been implemented in MATLAB 7.12.0. A set of test images like BMP and TIFF are taken to justify
the efficiency of the algorithm. Fig 3 shows the test images and their respective resulting compressed
images using BMP, TIFF and the proposed compression methods.
The experimental results with the proposed compression method have been arranged in the Table 1
and Table 2 for the different image processing images. It also shows the comparison between DCT
and the proposed compression method. From these tables, we find out the compression ratio of
different BMP and TIFF color and gray scale images with different resolutions and then finally to find
the PSNR and MSE value of images using DWT. So, the proposed compression technique gives the
better results compared to other compression methods.
a)Original barbara image(bmp) b)Compressed image (DWT)
c) Compressed image (DCT) d) Compressed gray scale image
Resolution: 512*512
Figure 3. A barbara image and corresponding compressed images in BMP format using DCT and
DWT methods.
Figure 2. The structure of image compression using wavelet
transforms
Image
Wavelet
Transform Quantization
Entropy
Encoding
Compressed
Image
Entropy
Decoding
Dequantization
Inverse
Wavelet
Transform
Reconstructed
Image

24
a)Original peppers image(tiff) b)Compressed image(DWT)
c) Compressed image (DCT) d) Compressed gray scale image
Resolution: 512*512
Figure 4. A pepper image and corresponding compressed images in TIFF format using DCT and
DWT methods.
Figure 5: Barbara image at decomposition Figure 6: Peppers image at decomposition
level 1 level 1

25
VII. CONCLUSION
In this research plan, an attempt has been made to study and compare the image compression
techniques using DCT and DWT. From the above experimental analysis, it is seen that in comparison
to the other experiments our experiment shows better performance with respect of compression size
and percentage of image compression. A new algorithm has been proposed on Image Compression
using DWT and Inverse DWT. An experimental result has been shown after compressing any color
image of different image formats. The most distinguishing feature of using DWT and Inverse DWT is
that it will not only enable to compress an image but also will help to maintain the quality of the
image as it was in its original form, which was not possible earlier in other image compression
techniques. The future direction of this research is to develop an algorithm using DCT or DWT where
any random image of any resolution or size could be compressed at a uniform rate without degrading
the quality of image.
Table I: Experimental Results of BMP Images at Decomposition Level 1
Original Image
Image
Resolution
Deco
mpo
sitio
n
Leve
l
Original
Image Size
(bytes)
Compressed Color
Image size Compressi
on Ratio of
Color
image
(%)
Compre
ssed
Graysca
le image
Size
Compressi
on Ratio of
Grayscale
image(%)
MSE
(%)
PSNR
(dB)
DCT
Propose
d
Method
(DWT)
Zelda256.bmp 256X256 1 196662 26838 9947 94.94 8395 95.73 20.85 32.71
Zebra256.bmp 256X256 1 196662 35608 15928 91.90 14342 92.71 27.02 33.81
House.bmp 256X256 1 196662 26065 10359 94.73 8743 95.55 20.98 34.91
Baboon256.bmp 256X256 1 196662 46812 19208 90.23 16809 91.45 29.56 71.92
Barbara512.bmp 512X512 1 786486 36967 45194 94.25 40494 94.85 16.31 36.01
Peppers.bmp 512X512 1 786486 35037 33529 95.74 27105 96.55 2.22 44.67
Lenna128.bmp 128X128 1 49206 25132 4488 90.88 3491 92.91 29.93 66.06
airplane.bmp 512X512 1 786486 33702 38679 95.08 33793 95.70 22.61 35.67
Boat.bmp 512X512 1 786488 35359 41951 94.67 37127 95.28 16.41 35.98
sailboat.bmp 512X512 1 786486 43994 52959 93.27 45788 94.18 7.47 31.37
Table II: Experimental Results of TIFF Images at Decomposition Level 1
Original Image Image
Resolution
Deco
mpo
sitio
n
Leve
l
Original
Image Size
(in bytes)
Compressed Color
Image size (bytes)
Compres
sion
Ratio of
Color
image
(%)
Compres
sed
Grayscal
e image
Size(byte
s)
Compres
sion
Ratio of
Grayscal
e image
(%)
MSE
(%)
PSNR
(dB)
DCT Propose
d
Method
(DWT)
Zelda256.tiff 256X256 1 196748 26838 9947 94.94 8395 95.73 14.85 32.71
Zebra256.tiff 256X256 1 177792 35608 15928 91.04 14342 91.93 27.02 33.81
House.tiff 256X256 1 196748 26006 10198 94.82 8464 95.70 6.67 31.44
Baboon64.tiff 64X64 1 13702 21957 1787 86.96 1277 90.68 4.50 26.53
Barbara512.tiff 512X512 1 667546 46967 45194 93.23 40494 93.93 16.30 36.01
Peppers.tiff 512X512 1 786572 36348 31308 94.75 33749 95.71 6.88 30.29
lena_512.tiff 512X512 1 2359350 366864 377881 95.21 32554 95.87 31.06 33.21
RGB.tiff 256X256 1 205884 13704 5956 97.12 3877 98.12 20.18 35.08
Woman_baby.tiff 512X512 1 799684 30067 20718 97.42 18122 97.81 4.35 41.75
strawberries.tiff 666X666 1 1339526 61933 59507 91.21 50576 96.23 43.80 231.7
1

26
REFERENCES
1. M.M.Chowdhury,and A.Khatun,”Image Compression using Discrete Wavelet Transform”,
International Journal of Computer Science Issues, Vol.9, Issue4, No.1, July 2012, ISSN
(online):1694-0814, www.IJCSI.org,pp.327-330.
2. Y.Sukanya, and J.Preethi, “Analysis of Image Compression algorithm using Wavelet
Transform with GUI in MATLAB”, IJERT: International Journal of Research in Engineering
and Technology, eISSN:2319-1163, pISSN:2321-7308, volume: 02, Issue: 10, Oct-2013,
www.ijret.org,pp. 595-603,2013.
3. A.Averbuch,D.Lazar, and M.Israeli,”Image Compressi” on using Wavelet Transform and
Multiresolution decomposition”,IEEE Transactions on Image Processing, Vol.5,No.1,
January, 1996.
4. K.H.Talukder, and K. Harada,”Haar Wavelet Based Approach for Image Compression and
Quality Assessment of Compressed Image”, IAENG International Journal of Applied
Mathematics, 36:1, IJAM_36_1_9, Advance Online Publication: 1 February, 2007.
5. N.Ahmed,T.Natarajan, and K.R. Rao,”Discrete Cosine Transform”,IEEE Transactions on
Computers, January, 1974, pp.90-93.
6. R.C.Gonzalez,R.E.Woods,Digital Image Processing, 2nd
Edition, Chapter 7,” Wavelet and
Multiresolution Processing”, pp.372-386.
7. B.Nilesh,S.Sachin,N.Pradip, and D.B.Rane,” Image Compression using DWT”,ISSN: 2249-
3433, IJCTEE,Volume3,Special Issue,March-April 2013.
8. A.H.M.J.I.Barbhuiya,M.Singha, and K.Hemachandran,”Wavelet Transformation and Its
Applications in Digital Image Processing”, In the proceeding of National Conference on
Latest Trends and Innovations in Computing, Electronics & Communication, held at AISECT
University, Bhopal, November, 17-18, 2011.
9. F.Z. Qureshi,"Image Compression Using Wavelet Transform", Computational Vision Project,
University of Toronto, http://citeseer.ni.nec.com/425270.html
10. S.Anandanarayanan, and S.K.Srivatsa, “A High Performance Novel Image Compression
Technique Using Huffman Coding with Edge Dection”, International Journal of Computer
Engineering & Technology (IJCET), ISSN 0976 – 6367(Print), ISSN0976 – 6375(Online),
Volume 4, Issue 2, March – April (2013), pp. 17-22, © IAEME: www.iaeme.com/ijcet.asp.
,2013.
11. A.H.M.J.I.Barbhuiya, and K.Hemachandran,” Wavelet Transformations and Its Major
Applications in Digital Image Processing”, International Journal of Engineering Research &
Technology (IJERT), ISSN: 2278-0181, Vol. 2 Issue 3, March, 2013.
12. S.Lawson and J.Zhu, “Image Compression using wavelets and JPEG2000: a tutorial”,
Electronics and Communication Engineering Journal, June 2002.
13. Laskar, Tahera Akhtar, and K. Hemachandran. "Digital Image Watermarking Techniques and
Its Applications." International Journal of Engineering Research and Technology. Vol. 2. No.
3 (March-2013). ESRSA Publications, 2013.
14. Pardeep Singh, Nivedita, Sugandha Sharma, “A Comparative Study: Block Truncation
Coding, Wavelet, Embedded Zerotree and Fractal Image Compression on Color Image”
International journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2,
2012, pp. 10 - 21, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
15. Hitashi and Sugandha Sharma, “Fractal Image Compression Scheme Using Biogeography
Based Optimization on Color Images” International journal of Computer Engineering &
Technology (IJCET), Volume 3, Issue 2, 2012, pp. 35 - 46, ISSN Print: 0976 – 6367, ISSN
Online: 0976 – 6375.

An approach for color image compression of bmp and tiff images using dct and dwt

More Related Content

What's hot

Similar to An approach for color image compression of bmp and tiff images using dct and dwt

More from IAEME Publication

Recently uploaded

An approach for color image compression of bmp and tiff images using dct and dwt