 Open Access
 Total Downloads : 10
 Authors : Dr. Anjali Mathur , Nitesh Agarwal , Dr. Sandeep Mathur
 Paper ID : IJERTCONV3IS23013
 Volume & Issue : NCETRASECT – 2015 (Volume 3 – Issue 23)
 Published (First Online): 24042018
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Image Compression using THV method in Lossless Image Compression & in Lossy Image Compression
Dr. Anjali Mathur1
Department of Mathematics
Jodhpur Institute of Engineering & Technology Jodhpur, India
Nitesh Agarwal2
Dr. Sandeep Mathur3
Department of Computer Science Jodhpur Institute of Engineering & Technology
Jodhpur, India
Department of Mathematics
Jodhpur Institute of Engineering & Technology Jodhpur, India
AbstractImage compression process reduce required storage size of image. Image compression process use two technique to compress image lossless image compression & lossy image compression. Images that provide numerical, secure & financial information compressed using lossless image compression because we required original data back after decompression process. Lossless image compression use some entropy encoding technique but its compression ratio is low w.r.t lossy image compression. But other images like multimedia images can be compressed using lossy image compression because the human eye is very tolerant of approximation error in an image. Hence we may decide to exploit this tolerance to produce increased compression, at the expense of image quality by reducing some pixel data or information. Using this concept this paper proposed a THV (Threshold Variable) method before entropy encoding technique to get more compressed size using lossless image compression as well as using lossy image compression & give a comparative study in which technique, proposed method is more useful. This paper deals with comparative study of a compressed image on the basis of different value of threshold variable used in THV method.
Key Words: THV, DCT, RLE, MSE, PSNR.

INTRODUCTION
A digital image is a 2D pixel matrix where each position of pixel gives a color information for image in bits format. On the basis of this bits format image is classified as 2 bit, 6 bit, 8 bit, 16 bit, 24 bit & 32 bit. When an image is design using one of these format each pixel store information in particular bit format in which they are build. To store image in only bits format some time required a high amount of storage device for example if an image is build using 8 bit format then each pixel must have 8 bit storage even it represent information that required less than 8 bit to store. As the digital devices has limited storage & transmission capability we need to compress the image by some suitable method to satisfy this limitation. For example when we play a YouTube video if our communication system have enough bandwidth to play YouTube high resolution video then we can play video without any buffering problem but if bandwidth is low then YouTube use some compression method & transfer the bits of video frame in compressed format according to our network bandwidth size. By lossless image compression we get original image in decompression process without any loss of pixel value. But other images like multimedia images can be
compressed using lossy image compression because the human eye is very tolerant of approximation error in an image. Hence we may decide to exploit this tolerance to produce increased compression, at the expense of image quality by reducing some pixel data or information.

Lossless Image Compression
Lossless image compression process compress the image for storage & communication purpose in such a way that original image can be retrieve during decompression process without any loss or modification of information (pixel bits). Lossless image compression use some entropy encoding techniques to compress digital image such as RLE (Run Length Encoding), Huffman Encoding, LZW (LempelZiv Welch) Encoding, Area Encoding etc. Process of lossless image compression is shown in fig 1 [14].
Image
Entropy Encoding
Image
Entropy Encoding
Image
Image
Channel Entropy Decoding
Fig 1 Lossless Image Compression
Entropy encoding give good compression ratio when image have repeated pixel value sequentially but all the image not have such type of repeated pattern hence present introduce a THV module before encoding technique to make a repeated sequence [5].
Image
Proposed THV Method
RLE
Image
Proposed THV Method
RLE
Channel
Image with Some Pixel modification
Inverse
Image with Some Pixel modification
Inverse
Fig 2 Proposed Lossless Image Compression
In proposed method as shown in fig 2 image in pixel format transfer to the THV module & then send to the entropy where using RLE proposed method compress input image. THV module modify pixel values of image to get repeated sequence hence output image after the decoding process is equivalent to original image but not equal to the original image data.

THV (Threshold Variable) Method
THV method take pixel value from 2D pixel matrix row by row. THV method uses two node 1st node store the 1st pixel & 2nd node move forward row by row in pixel matrix. THV method take pixel difference between pixel value store in these two node & repeat the pixel value store in node 1 until difference between these two nodes are not greater than threshold variable t used in THV method. One a difference greater than t occurs node 1 store that pixel & node 2 traverse pixel one by one from neighboring pixel of pixel store in node 1 & same process is follow until complete pixel matrix not traversed. For example
101
100
105
110
201
200
205
210
306
304
307
306
406
404
407
406
502
501
500
502
602
601
600
602
728
729
730
732
828
829
830
832
201
205
202
203
905
904
900
902
555
554
553
550
551
552
557
558
602
605
604
602
601
701
705
709
905
905
905
905
905
906
912
911
Table 1: Pixel matrix of input image
Table 1 shows a 2D pixel matrix but this matrix cannot be compressed using RLE because pixel value not repeated sequentially. THV method convert this matrix so that it can be compressed using RLE. Let the value of variable t in THV method is 10 then converted pixel matrix is
602
101
101
101
101
201
201
201
201
306
306
306
306
406
406
406
406
502
502
502
502
602
602
602
728
728
728
728
828
828
828
828
201
201
201
201
905
905
905
905
555
555
555
555
555
555
555
555
602
602
602
602
602
701
701
701
905
905
905
905
905
905
912
912
Table 2: Pixel matrix of After THV method with
variable t =10
After the THV method pixel matrix contain a good no of repeated pixel as shown in table 2. This repeated pixel helps the RLE to compress pixel matrix.
THV algorithm
Input: Pixel matrix of input image. Output: Modified Pixel Matrix.
{
h = height of pixel matrix;
pixel[h][w] = pixel matrix of original image; t = THV variable;
for(i=0; i<h; i++)
{
j=0;
tmp=pixel[i][j]; for(j=0; j<w; j++)
{
if( (difference between tmp & pixel[i][j]) > t
) pixel[i][j] = tmp;
else tmp=pixel[i][j];
}
}
} [1]
THV method used in such type of image where modifying some pixel data does not cause any big problem.

RLE (Run Length Encoding)

This is a very simple compression technique method used for compressing sequential data. Many digital image consist pixel values that are repeats sequentially for such type of image RLE is useful. In proposed THV method RLE receive sequential data from pixel matrix modified by THV method & store pixel value that repeats & no of time that pixel value repeat sequentially. For example table 2 by RLE compressed as
Pixel Value
Repetition
Pixel Value
Repetition
101
4
201
4
201
4
905
4
306
4
555
8
406
4
602
5
502
4
701
3
602
4
905
6
728
4
912
2
828
4
Table 3: Compressed Data after RLE
Table 3 required less storage space as compare to table 1. Table 1 require total 64 values to store but table 3 require only 30 values to store [5].
Compression Ratio (CR) = 64/30 =
2.13 1.2 Lossy Image compression
Lossy image compression gives a high compression ration then lossless image compression. It is say as lossy because it modify or destroy some pixel information by using some transformation like DCT, DST, KLT, DFT etc. & using quantization table before entropy encoding.
w = width of pixel matrix;
Fig 3: Lossy Image Compression
In this paper we use DCT (Discrete Cosine Transformation) to transform image in frequency domain [4].

DCT (Discrete Cosine Transform) [11]
DCT convert an image into its equivalent frequency domain by partitioning image pixel matrix into blocks of size N*N. An image is a 2D pixel matrix hence 2D DCT is used to transform an image.
2D DCT can be defined as
N 1 N 1 2x 1u 2y 1v
Where (i,t) define position of input & output value, QDCT is DCT block after quantization, QT is standard quantization matrices & defined as
Cu,vu (v) f x, ycos cos
x0 y 0 2N 2N
for u, v = 0,1,2,,N 1.
& inverse transformation is defined as
N 1 N 1 2x 1u 2y 1v
1
16
11
10
16
24
40
51
61
12
12
14
19
26
58
60
55
14
13
16
24
40
57
69
56
14
17
22
29
51
87
80
62
18
22
37
56
68
109
103
77
24
35
55
64
81
104
113
92
49
64
78
87
103
121
120
101
72
92
95
98
112
100
103
99
16
11
10
16
24
40
51
61
12
12
14
19
26
58
60
55
14
13
16
24
40
57
69
56
14
17
22
29
51
87
80
62
18
22
37
56
68
109
103
77
24
35
55
64
81
104
113
92
49
64
78
87
103
121
120
101
72
92
95
98
112
100
103
99
f x, y u(v)cu,vcos
u 0 v0
cos
2N 2N
2
1.2.3 Proposed THV method in Lossy Image Compression
Where Cu, v represents frequency value for u, v &
f x, y represents pixel color value at position ( x, y ).
1 for u 0
Proposed THV method can be used in Lossy Image compression before entropy encoding as
(u)
N 3
2
N
for u 0
(v)
1 for v 0
N 4
Fig 4: Lossy Image Compression with THV Method
The THV algorithm is work in same way as in lossless image compression the only difference is that in lossless image
2
for v 0
compression it take input from image pixel matrix but in lossy
N

Quantization
A Quantizer simply reduces the number of bits needed to store the transformed coefficients by reducing the precision of those values. Since this is a manytoone mapping, it is a lossy process and is the main source of compression in an encoder.
The quantization matrix is designed to provide more resolution to more perceivable frequency components ove less perceivable components (usually lower frequencies over high frequencies) in addition to transforming as many components to 0, which can be encoded with greatest efficiency. A DCT block is quantize using following formula
DCT (i, j)
QDCTi, j ROUND 5
QT (i, j)
& this QDCT block dequantize by following formula
DCTi, j ROUND QDCT(i, j) *QT(i, j) 6
For i, j= 0, 1, 2, 3.,N1
image compression it take input from quantize block & loop in THV depend upon dimension of quantize block not on dimension of image.


MAIN RESULTS & OUTPUTS

Implementation of Proposed THV method in lossless Image Compression
Steps involved in this implementation

Create pixel matrix of the image.

Apply THV method on pixel matrix & apply THV algorithm.

Use RLE as entropy encoding on pixel matrix obtain from THV algorithm.

Store matrix obtain by RLE method in to secondary storage.

To get required image read encoded matrix from secondary storage & apply entropy decoding (Run Length Decoding) on that encoded matrix.

Using this decoded matrix make pixel matrix & then using this pixel matrix make required image.

Now we Find MSE (Mean Squared Error), PSNR (Peak Signal To Noise Ratio) & CR (Compression Ration) to determine quality of image obtain by

proposed method for each t variable used in THV method. MSEX, PSNRX & CRX calculated by following formulas [12] –
1
1
H 1 W 1
MSEt
[O(x, y)M t (x, y)]2 7
H *W x0 y0
PSNRt=20*log10 (MAX) – 10*log10 (MSEt) (8)
CR
CR
Original Im age size
t Output Im age size 9
Where H=Height of Image, W= Width of Image, variable MAX shows max value of a pixel for example here image is 8 bit hence MAX=255, MSEt, PSNRt & CRt is MSE, PSNR & CR at variable t used in THV method.
Quality of image obtain by proposed method is depend on MSEt & PSNRt value. If as the MSE value increases PSNR value decreases then we get a bad quality of image by proposed method & if as the MSE value decreases PSNR value increases we get a batter quality image hence on basis of this MSEt & PSNRt value proposed method gives a best value of X on which we get a high compressed image with best quality.
2.1.1 Outputs
2.1.1.1 Lossless image compression without THV method
Lossless Image Compression
Uncompressed Compressed Image Image Size=768 KB Size=343 KB
Fig 5: Lossless Image Compression without THV Method
MSE 
PSNR 
CR 

Lossless Image Compression without THV Method 
0 
Infinity 
2.24 
Table 6: MSEt, PSNRt, CRt on different value of t

Graphs

THV variable t vs. CRt
Fig 7: Variation in CRt with different value of THV variable t

THV variable t vs. MSEt
Fig 8: Variation in MSEt with different value of RVM
variable t
Table 5: MSE, PSNR, CR without THV method
2.1.1.2 Lossless Image Compression with proposed THV method

THV variable t vs. PSNRt
t=1
t=2
Size=247 KB
Uncompressed t=3 Image
Size = 768 KB
Size=191 KB
Size=159 KB
Fig 9: Variation in PSNRt with different value of THV
variable t

THV variable t vs. PSNRt & MSEt

t=4
Size=128 KB
Compressed Images
Fig 6: Proposed Lossless Image Compression with variation
Fig 10: Variation in PSNRt & MSEt with different
value of THV variable t
in THV Variable t

Implementation of Proposed THV method in lossy Image Compression
Steps involved in this implementation

Create pixel matrix of the image & divided it into blocks of size 8*8

Apply FDCT (Forward Discrete Sine Transform) on each 8*8 block of pixel matrix to get equivalent 8*8 DCT blocks using eq (1).

Apply eq (5) on each block of DCT to get QDCT block.

Apply THV algorithm on each block of QDCT to get THV block.

Combine each THV block & apply RLE on combine block & store this encoded block on secondary storage.

To get required image read encoded matrix from secondary storage & apply entropy decoding (Run Length Decoding) on that encoded matrix.

Divide this decoded matrix in to blocks of size 8*8.

Apply eq (6) on each block to get DCT blocks.

Apply eq (2) on each DCT block to get IDCT blocks.

Combine all IDCT blocks to get pixel matrix.

Using pixel matrix we get required image.

Now we Find MSE (Mean Squared Error), PSNR (Peak Signal To Noise Ratio) & CR (Compression Ration) to determine quality of image obtain by proposed method for each t variable used in THV algorithm.

Outputs

Lossy Image compression without THV method
Lossy Image Compression
Uncompressed Image Compressed Image
Size=768 KB Size=71.9 KB
Fig 11: Lossy Image Compression without THV Method
MSE
PSNR
CR
Lossy Image Compression without THV Method
7.37
39.45
10.68
Table 7: MSE, PSNR, CR without THV

Lossy Image Compression with proposed THV
method t=1
Table 8: MSEt, PSNRt, CRt on different value of t

Graphs

THV variable t vs. CRt
Fig 13: Variation in CRt with different value of THV variable t

THV variable t vs. MSEt
Fig 14: Variation in MSEt with different value of RVM
variable t

THV variable t vs. PSNRt
t=2
Uncompressed t=3 Image Size=768 KB
Size=47.9 KB
Size=39.9 KB
Size=39.9 KB
Fig 15: Variation in PSNRt with different value of THV variable t

THV variable t vs. PSNRt & MSEt



t=4
Size=31.9 KB
Fig 16: Variation in PSNRt & MSEt with different
value of THV variable t
Compressed Images
Fig 12: Lossy Image Compression with THV Method
3. CONCLUSION
The result presented in this document shows that

The results shows that as the value of variable t increases storage size of image decreases as shown in Fig 6, Fig 12, Table 6 & Table 8.

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As the value of t increases CRt also increases as in lossless image compression as shown in Fig 7 but in lossy image compression as the value of t increases

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CRt goes to a constant value.


As the value of t increases proposed process add more noises in the image i.e. value of MSEt increases as shown in Fig 8 & Fig 14.

The results shows that proposed method give good results in lossless image compression but in lossy image compression present method not works well because it add high aount of noises in image that cannot be tolerable this can be seen by comparing Table 6 & Table 8.

As the value of t increases PSNRt value decreases as shown in Fig 9 & Fig 15.

As the MSEt value decreases & PSNRt increases quality of image improves but CRt decreases.

Fig 13 show after value 3 of variable t CRt almost constant in lossy image compression.

Image used in this paper for lossless image compression have almost same MSEt & PSNRt value for t=15 as shown in Fig 10 .

Lossy image compression with THV method give more compression ratio than lossless image compression with THV method but lossless image compression with THV method give good results because its MSEt values are less than MSEt values in lossy image compression with THV for same value of t.

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