A Low Space Bit-Plane Slicing Based Image Storage Method using ...

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 4, April 2012)

694

A Low Space Bit-Plane Slicing Based Image Storage Method using

Extended JPEG Format

Santanu Halder1, Debotosh Bhattacharjee

2, Mita Nasipuri

2, Dipak Kumar Basu

2

1 Department of Computer Science and Engineering, Govt. College of Engineering and Textile Technology, Berhampore, India

Email: [email protected] 2Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

Email: {[email protected], [email protected], [email protected]} *Former Professor

Abstract— In this paper, we propose a novel bit-plane

slicing based lossy image compression technique in Extended

JPEG format which takes less storage space than JPEG

format with no visual degradation. One of its main features is

to find out and discard those particular bits which are not

responsible for the color and texture information of the image,

which allows storing the image with less number of bits which

saves the storage space and also improves the data

transmission rate for the image. The proposed method has

been tested on 450 images and in most of the cases the images

take very less storage space than the normal jpg images.

Index Terms— Psychovisual Redundancy, Bit-Plane Slicing,

JPEG format, Extended JPEG format

I. INTRODUCTION

An image is essentially a 2-D signal processed by the

human visual system. The signals, representing images, are

usually in analog form. However, for processing, storage

and transmission by computer applications, they are

converted from analog to digital form. A digital image is

basically a 2-Dimensional array of pixel intensities. There

are a lot of works available in literature on both lossless and

lossy image compression techniques [1-7]. Image

compression addresses the problem of reducing the amount

of data required to represent a digital image while keeping

the resolution and the visual quality of the reconstructed

image as close to the original image as possible. It is a

process intended to yield a compact representation of an

image, thereby reducing the image storage/transmission

requirements. Image compression techniques reduce the

number of bits required to represent an image by taking

advantage of the three basic data redundancies:

1. Coding Redundancy

2. Interpixel Redundancy

3. Psychovisual Redundancy.

Coding redundancy is present when less than optimal

code words are used. Interpixel redundancy results from

correlations between the pixels of an image. Psychovisual

redundancy is due to data that is ignored by the human

visual system (i.e. visually non essential information).

The proposed work concentrates on the Psychovisual

redundancy to reduce the required storage space for an

image.

An inverse process called decompression (decoding) is

applied to the compressed data to get the reconstructed

image. The benefits of compression can be summarized as

follows:

1. It reduces the data transmission cost as less number of

data is to be transferred over network.

2. It not only reduces storage requirements but also

overall execution time.

3. It also reduces the probability of transmission errors

since fewer bits are transferred.

4. It also provides a level of security against illicit

monitoring.

The image compression techniques are broadly classified

into two categories depending on whether or not an exact

replica of the original image could be reconstructed using

the compressed image. These are:

1. Lossless technique

2. Lossy technique

The proposed work focuses on a lossy compression

technique based on bit plane slicing method.

A) Related Work

Most commonly used device independent raster image

formats are BitMap format (BMP), Graphics Interchange

Format (GIF), Joint Photographic Expert Group format

(JPEG), Microsoft Image eXtension format (MIX), Portable

Network Graphics format (PNG), Tagged Image File

format (TIFF) etc [8]. Among of these formats, the JPEG

format (.jpg file extension) is most common file format for

images. It can be used to display high quality photographs,

or pictures containing million of colors. It can efficiently

compress large, high quality photos into very compact files.

In brief the JPEG compression coding steps are as follows

[8]:



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Color space transform

Sampling

Level shifting

DCT transformation

Quantization

Zig-zag encoding

Run length encoding

Frequency encoding

B) Our Approach

In this paper, we develop a new bit plane slice based

lossy compression technique, called Extended JPEG

compression method, which is very efficient to store an

image using less number of bits. It not only saves storage

space but also reduces data transmission cost over the

network. The proposed work first compresses the image

using JPEG format and then apply the proposed bit plane

slicing based lossy compression technique to further

compress the image with no visual degradation. The steps

for the compression technique are summarized as follows:

Step 1: Read the input image file in jpg format.

Step 2: If the image is color one, then find its gray level

image.

Step 3: Get the bit plane images of the gray level image

for Bit 0 (LSB) to Bit 7 (MSB).

Step 4: Discard those bits for which the bit plane images

contribute very little information and thus no visual

degradation.

This paper is organized as follows: The Section II

describes the Bit-Plane slicing method. The Section III

describes the image compression technique. The Section IV

depicts the method to decompress the image. Section V

shows the experimental results and finally Section VI

concludes and remarks about some of the aspects analyzed

in this paper.

II. BIT-PLANE SLICING METHOD

Bit-Plane Slicing is a technique in which the image is

sliced at different planes. It ranges from Bit level 0 which is

the least significant bit (LSB) to Bit level 7 which is the

most significant bit (MSB) as shown in Fig. 1.

Fig. 1: Bit Plane Slicing

It is clear that the intensity value of each pixel can be

represented by an 8-bit binary vector (b7, b6, b5, b4, b3, b2,

b1, b0) bk, where k is from 0 to 7 and each bk is either “0”

or“1”. In this case, an image may be considered as an

overlay of eight bit-planes. Each bit-plane can be thought of

as a two tone image and can be represented by a binary

matrix [9] [10].

The formation of bit plane is given by Eq. (1).

)],(2

1[

2

1),( jiIfloorRjiIBP

kk

………………(1)

Where I(i,j) = original image, IBPk(i,j) = bit-plane

information for bit k, R = remainder.

Fig. 2 shows the bit plane images for bit 0 (LSB) to bit 7

(MSB) of some images.

(a)

k=0 k=1 k=2 k=3

(a0) (a1) (a2) (a3)



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k=4 k=5 k=6 k=7

(a4) (a6) (a7) (a8)

(b)

k=0 k=1 k=2 k=3

(b0) (b1) (b2) (b3)

k=4 k=5 k=6 k=7

(b4) (b5) (b6) (b7)

Fig. 2: Some Images and their Bit Plane Images

Now, for the image of Fig. 2(a), bit plane images with

k=0, k=1 and k=2 does not contribute so much information

in image formation. Experimental result shows that the

image can be stored with the information provided by bit3

to bit7 only keeping the originality of the image unchanged.

Thus number of bits per pixel can be reduced to 5 from 8

which save more storage space. Similarly, the image of

Fig. 2(b) can be stored omitting the information for bit 0

and bit 1 and hence the number of bits per pixel for this

image can be reduced to 6 bits from 8 bits.

III. IMAGE COMPRESSION

This section describes the method to compress an image

in Extended JPEG format. First the image, which is already

compressed in JPEG format, is taken and then Algorithm 1

is applied to get the compressed image in proposed format.

Algorithm 1

Algorithm Image Compression

// I is source image and I1 is compressed image. Let I is a

color image. Factor = 2j where 0 ≤ j ≤ 7.

{

Input an Image I.

Set Threshold = T and Factor = 256.

Do

{

for each row i of image I

{

for each column j of image I

{

I1(i,j,1) = I(i,j,1)/Factor; // Set R values

I1(i,j,2) = I(i,j,2)/Factor; // Set G values

I1(i,j,3) = I(i,j,3)/Factor; // Set B values

}

}

Calculate the Euclidean distance D between I and I1

Factor = Factor/2;

}while(D>T);

}//End of Algorithm

Fig. 3 shows some images after applying Algorithm1.

Each image is tagged with the size of the image.

Size: 117 KB Size: 20.7 KB

(a) (b)

Size: 141 KB Size: 40.7 KB

(c) (d)

Fig. 3: Some Color Images and their compressed images after

applying Algorithm 1. (a) Original Images in JPEG format (b)

Compressed images in Extended JPEG format after applying

Algorithm 1

IV. IMAGE DECOMPRESSION

Image decompression is a technique to reconstruct the

original images from the compressed ones. In section III,

the proposed method for Extended JPEG format on any

JPEG image has been discussed. In this section, the method

to decompress the image in Extended JPEG format is

illustrated. Algorithm 2 depicts the process.

Algorithm 2

Algorithm Image Decompression



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// I1 is compressed image and I2 is retrieved image. Let

I1 is a color image.

{

Input a compressed image I1.

for each row i of image I1

{

for each column j of image I1

{

I2(i,j,1) = I1(i,j,1)*Factor; // Regain R values

I2(i,j,2) = I1(i,j,2)*Factor; // Regain G values

I2(i,j,3) = I1(i,j,3)*Factor; // Regain B values

}

}

}//End of Algorithm

Fig. 4 shows some retrieved images from the compressed

images using Algorithm 2.

(a) (b)

(c) (d)

Fig. 4: Some compressed Images followed by the retrieved images

using Algorithm 2. (a) Compressed Images using Algorithm 1 (b)

Decompressed Images using Algorithm 2

V. EXPERIMENTAL RESULTS

For testing the proposed method, we had about 450

images with different intensity levels. Some images are

taken from the standard images provided by Windows

sample pictures. Other images are taken from our own

database DB-JU-I. The method has been tested using

Matlab 6.0. Some results are shown in Fig. 5. Each image is

associated with size required to store the image. Factor = 2j

indicates that j LSB bits can be discarded as their bit plane

slicing images don’t contribute too much information in

image formation.

Alternatively, discarding that j LSB bits doesn’t degrade

the visual effect of the image.

Size: 892 KB Size: 241 KB Factor = 8


Size: 120 KB Size: 48.5 KB Factor = 4

Size: 90.8 KB Size: 38.8 KB Factor = 4







698





Size: 1433.6 KB Size: 264 KB Factor = 16




(a) (b) (c)

Fig. 5: Some results after compression of images using JPEG method and

Extended JPEG method with required storage space. (a) Original images

stored in JPEG format (b) Compressed images using Extended JPEG

format (c) Decompressed Images those are compressed using Extended

JPEG format

Fig. 6 shows the comparison result of Extended JPEG

format Vs JPEG format in terms of Storage space.

Performance of Extended JPEG Format Vs JPEG Format in

terms of Storage Space

0

200

400

600

800

1000

1200

1400

1600

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37

Image Number

Sto

rag

e S

pace (

in K

B)

JPEG Format

Extended JPEG Format

Fig. 6: Comparison result of Extended JPEG format Vs JPEG format

in terms of storage space

VI. CONCLUSION

In this paper, we propose a novel bit plane slicing based

lossy compression technique which takes much less storage

space than jpeg compression. Given an image in JPEG

format, we first convert it into gray level image (if the

image is color one) followed by finding its bit plane images

for bit 0 to bit 7, discard those bits for which the bit plane

images don’t give so much information by dividing the

pixel intensities by a factor of 2j and thereby store the

image as compressed one. For decompression, the pixel

intensities are regained by the reverse process. Our

approach is tested on a database including about 450

images. This work can be useful for the image compression

with no visual degradation.

ACKNOWLEDGMENT

Authors are thankful to the "Center for Microprocessor

Application for Training Education and Research", "Project

on Storage Retrieval and Understanding of Video for

Multimedia" of Computer Science & Engineering

Department, Jadavpur University, for providing

infrastructural facilities during progress of the work. One of

the authors, Mr. Santanu Halder, is thankful to Government

College of Engineering and Textile Technology,

Berhampore, for kindly permitting him to carry on the

research work and Dr. D. K. Basu acknowledges the thanks

to AICTE, New Delhi for providing an Emeritus

fellowship.



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REFERENCES

[1] Ming Yang & Nikolaos Bourbakis ,“An Overview of Lossless

Digital Image Compression Techniques,” Circuits & Systems, 2005

48th Midwest Symposium, vol. 2 IEEE ,pp 1099-1102,7 – 10 Aug, 2005.

[2] Milos Klima, Karel Fliegel, “Image Compression Techniques in the

field of security Technology: Examples and Discussion”, Security Technology, 2004, 38th Annual 2004 International Carnahan

Conference, pp 278-284,11-14 Oct., 2004.

[3] Ismail Avcibas, Nasir Memon, Bulent Sankur, Khalid Sayood, “A Progressive Lossless / Near Lossless Image Compression

Algorithm”, IEEE Signal Processing Letters, vol. 9, No. 10, pp 312-

314, October 2002. [4] C.K. Li and H.Yuen, “A High Performance Image Compression

Technique For Multimedia Applications”, IEEE Transactions on

Consumer Electronics, Vol. 42, no. 2, pp 239-243, 2 May 1996. [5] Wen Shiung Chen, en- HuiYang & Zhen Zhang, “ A New Efficient

Image Compression Technique with Index- Matching Vector

Quantization,” Consumer Electronics, IEEE Transactions, Vol. 43, Issue 2, pp 173- 182, May 1997.

[6] David H. Kil and Fances Bongjoo Shin, “Reduced Dimension Image

Compression And its Applications”, Image Processing, 1995, Proceedings of International Conference, Vol. 3, pp 500-503, 23-26

Oct., 1995.

[7] Vo Dinh Minh Nhat, Sung Young Lee, “Two- Dimensional Weighted PCA algorithm for Face Recognition”, Proceedings 2005

IEEE International Symposium on Computational Intelligence in

Robotics and Automation, pp 219-223, June 27-30,2005, Espoo, Finland.

[8] Malay K. Pakhira, Computer Graphics Multimedia and Animation

2nd Ed, PHI. [9] R. C. Gonzalez, and R. E. Woods, Digital Image Processing 2nd ed.,

Prentice Hall, Inc., New Jercy.

[10] K. C. Ting, D.B.L. Bong, Y.C. Wang, “Performance Analysis of Single and Combined Bit-Planes Feature Extraction for Recognition

in Face Expression Database”, Proceeding of the International

Conference on Computer and Communication Engineering 2008, May 13-15, 2008 Kuala Lumpur, Malaysia.

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