Bit Plane Technique for Power Concerned Environment 1

Journal of Engineering Technology Volume 2, Jan. 2014, Pages 49-57

www.joetsite.com

Bit Plane Technique for Power Concerned Environment

Gwanggil Jeon

Department of Embedded Systems Engineering, Incheon National University,

119 Academy-ro, Yeonsu-gu, Incheon 406-772, Korea,

Abstract. An energy restricted image processing method under power constrained

condition is important. In this paper, we analyze bit plane analysis and apply this method

to energy efficient image processing method. We investigate number of bit cases and

their corresponding object and subjective performance such as CPSNR and S-CIELAB.

Experimental results indicate that the presented method can raise energy efficiency

significantly while sustaining image quality perceptual quality.

Keywords: Bit plane, most significant bit, least significant bit, image enhancement,

energy efficient method, low power image processing.

1 INTRODUCTION

The swift progression of signal processing has had it comfortable to process digital

pictures. However, sometimes we obtain poor quality pictures due to limited storage of

image system. Enhancing images is important issue which includes sharpness using

unsharp masking, noise level adjustment using denoising filter, color accuracy

enhancement using super resolution or color interpolation, and contrast enhancement

using histogram equalization [1-3]. Among them, bit plane is an important tool to save

signal storage with giving a satisfactory perception quality to viewers [4]. Bit plane is

widely known in signal processing. In addition to save storage, bit plane is good for

energy restriction (power saving) which is important for multimedia digital devices such

as mobile phones, TV, and display panels [5]. As consumers prefer to large size display

panels, therefore a great amount of energy is used up by displaying process [6]. To do

end, we proposed energy restriction method for general image processing using bit

plane characteristics [9-19].

In this paper, we propose bit plane skipping process. It is known that lower bit plane

represent random noise while higher bit plane represents meaningful information.

Therefore, to save storage, it is possible to remove lower bit plane from processed

image. Based on this idea, our proposed method gives few merits. For instance, the

proposed method can reduce the error and cost during the transmission over network.


www.joetsite.com

Also, the proposed method reduces the storage, consumed time and power. Finally, our

method can help the security issue.

The rest of this paper is organized as follows. In Section 2, we explain bit plane

slicing technique and the bit plane combination. Section 3 describes experimental

results and conclusion remarks are shown in Section 4.

2 BIT PLANE SLICING AND ITS COMBINATION

BP0

BP1

BP2

BP3

BP4

BP5

BP6

BP7

Gra

y s

cale

im

age

LSB

MSB

Figure. 1. Example of bit plane slicing for gray image.

Digitally, an image is represented in terms of pixels and each pixel has number of

bits. The bit plane of a digital image is a set of bits match a provided bit position in each

of the binary numbers representing the image. A pixel in a gray level image is

composed of eight bits, and each bit contributes different level of intensities. Let us

assume that each pixel in an image is described by n bits. Then the image is composed

of eight bits, each bit may have either 0 or 1, and each bit plane ranges from bit plane 0

(BP0) and bit plane n-1 (BPn-1). Here we assume BP0 as least significant bit (LSB) and

BPn-1 as most significant bit (MSB). For example, if an signal is a gray image, then BP0

contains all least important bits in the bytes making the pixels in the image and BP7

includes all most important order bits. Figure 1 shows an example of bit plane slicing

for gray image.

140 159

167 154

10001100 10011111

10100111 10011010

1 1

1 1

0 1

1 0

(a) (b) (c) (d)

Figure. 2. Example of bit plane slicing and MSB and LSM: (a) intensities of original 2x2 image, (b)

intensities in binary values, (c) most significant bit, and (d) least significant bit.


www.joetsite.com

(a) (b) (c)

(d) (e) (f)

(g) (h) (i)

Figure. 3. Bit plane slicing on gray scale flower image: (a) original image, (b-i) 0th bit plane (LSB) to 7th bit

plane (MSB).

Let us assume an image with 2x2 size and each pixel has intensities of Fig. 2. As

shown in Fig. 2, we assume four pixels have intensities of 140, 259, 167, and 154. Then,

these intensities are decomposed with binary values, such as “10001100,” “10011111,”

“10100111,” and “10011010.” The binary value at the leftmost position is MSB, „1, 1, 1,

1,‟ and the binary value at the rightmost position is LSB, „0, 1, 1, 0.‟

Figures 3 and 4 show examples of bit plane slicing, where 8 bit gray scale image

and its color image are used. As we can see, lower bit plane looks like random noise

while higher bit plane loos show significant information.

(a) (b) (c)


www.joetsite.com

(d) (e) (f)

(g) (h) (i)

Figure. 4. Bit plane slicing on color scale flower image: (a) original image, (b-i) 0th bit plane (LSB) to 7th bit

plane (MSB).

As lower bit plane looks like random noise, we can remove them to reduce the

storage purpose. In other words, gray scale image uses 8 bit planes which is represented

as BP07. Here we suggest BPk7 which represents gray scale image with kth

bit plane to 7th

bit plane (MSB) as shown in Fig. 5. The proposed method has few advantages. For

example, our method can reduce the image broadcasting price by removing unnecessary

bit plane, and less important information is omitted to be transferred. Therefore,

network usage is saved. In addition, as the number of transferred information is reduced,

transmission error may be reduced. This may help security issue. Moreover, the

proposed method reduces the computation consumption time and needed storage, also

power is saved.

BP

07

BP

17

BP

27

BP

37

BP

47

BP

67

BP

77

BP

57

LSB

MSB


www.joetsite.com

Figure. 5. Proposed bit plane skipping method.

3 SIMULATION RESULTS

(a) (b) (c) (d)

(e) (f) (g) (h)

Figure. 6. Visual performance comparison on 8th McM image.

To perform the proposed algorithm, we used 18 McM images [7] with different

intensity strength levels. Three images were employed for subjective comparison, and

they are #7, #8, and #9. Visual performance comparison is shown in Figs. 6 and 7.

Figure 8 shows the difference between original image and BP27 and BP37.

(a) (b) (c) (d)

(e) (f) (g) (h)


www.joetsite.com

Figure. 7. Visual performance comparison on 9th

McM image.

(a)

(b)

Figure. 8. Three images’ difference between original image and (a) BP27 and (b) BP37.

(a)

(b)


www.joetsite.com

(c)

Fig. 9. Performance comparison on three metrics: (a) CPSNR, (b) S-CIELAB, and (c)

CMSE.

We used three objective performance metrics, CPSNR (color peak-signal-to-noise

ratio), S-CIELAB [8], and CMSE (color mean squared error). In addition, size of file is

used to evaluate the performance. CPSNR is color version of PSNR, which is defined

via the CMSE.

Table 1. Averaged Results on Three Objective Metrics

Metric BP17 BP27 BP37 BP47 BP57 BP67 BP77

CPSNR 33.364 18.544 11.502 8.761 6.551 4.435 1.916

S-CIELAB 0.849 5.439 21.703 34.938 48.946 65.872 89.657

CMSE 502.7 2337.4 8021.6 13240.2 19232.3 27828.7 44245.7

Table 2. Image File Size of Different Combination of Bit Plane

Bit planes

Image # BP77 BP67 BP57 BP47 BP37 BP27 BP17 BP07

1 376156 576590 672658 714420 725538 739244 752476 753926

2 181402 442692 591128 670824 727424 751766 756186 756958

3 185600 345758 492644 623770 702836 738010 750162 753750

4 87508 215330 279986 456934 565502 654046 717494 748756

5 274046 457006 605402 710440 740982 755884 756532 757032

6 283682 384934 519316 658652 725502 742852 753310 756426

7 92382 186922 303924 466672 621686 720756 754644 756940

8 44838 105942 191032 300438 491588 704472 756670 757236

9 272894 537050 678036 734256 749740 753036 756178 756920

10 304418 566364 652778 706368 740922 746878 755754 756880


www.joetsite.com

11 211420 486110 636698 716132 746708 751732 755874 756848

12 586414 715350 746088 756380 756986 757008 757008 757004

13 710380 734232 752922 756090 756754 756966 756994 757002

14 273712 473712 628300 717850 745866 749904 755974 756870

15 268244 538928 647424 707106 738292 749312 754666 756686

16 248476 529070 675842 733714 754852 757010 757002 757004

17 245414 533542 673046 727646 750850 755614 756814 756984

18 195122 463424 538252 659718 708722 746252 754254 756920

Average 269006 460720 571415 656523 708375 740597 753222 756119

1 1

2

0 0

1[ ( , ) ( , )]

m n

i j

MSE ori i j rec i jmn

(1)

2

10

25510logPSNR

MSE

(2)

where ori is a noise-free m×n gray scale image and rec is a reconstructed image.

Figure 9 and Table 1 show objective performance comparison on three metrics,

CPSNR, S-CIELAB, and CMSE. As we can see, we use more bit planes, objective

performance increases significantly.

Table 2 shows the file size of each combination of bit planes for 18 McM images.

4 CONCLUSIONS

This paper proposed a new energy restricted image processing method under power

constrained condition. We analyzed bit plane and applied bit plane skipping method to

achieve some merits such as low storage, low power, less error, and less cost.

Simulation results show that the proposed method is feasible and the obtained file size

has been reduced successfully.

ACKNOWLEDGMENT

This work was supported by the National Research Foundation of Korea(NRF)

Grant funded by the Korean Government(MSIP)(2014025627)

REFERENCES

1. Yang, M., Bourbakis, N., “An overview of lossless digital image compression techniques,” in

Proc. IEEE Circuits & Systems 2005, 2(1099-1102), 7-10 Aug, 2005.

2. Avcibas, I., Memon, N., Sankur, B., Sayood, K., “A progressive lossless / near lossless image

compression algorithm,” IEEE Signal Processing Letters, 9(10): 312-314, October 2002.


www.joetsite.com

3. Cox, I., Miller, M., Bloom, J., Fridrich, J., Kalker, T., Digital Watermarking and

Steganography, 2nd ed., Morgan Kaufmann Publishers Inc., 2002.

4. Kil, D. H., Shin, F. B., “Reduced Dimension Image Compression and its Applications,” IEEE

ICIP1995, 3: 500-503, 23-26 Oct., 1995.

5. Chen, W. S., Yang, E. H., and Zhang, Z., “A new efficient image compression technique with

index- matching vector quantization,” IEEE Trans. Consumer Electronics, 43(2): 173- 182,

May 1997.

6. Shi, X., Liu, F., Gong, D., Jing, J., “An authentication watermark algorithm for JPEG

images,” in Proc. International Conference on Availability, Reliability and Security (ARES

'09), pp. 584-588, 16-19 March 2009.

7. Zhang, L., Wu, X., Buades, A., Li, X., “Color demosaicking by local directional interpolation

and non-local adaptive thresholding,” Journal of Electronic Imaging, 20: 023016, 2011,.

8. Zhang, X., Wandell, B. A., “A spatial extension of CIELAB for digital color image

reproduction,” J. Soc. Inf. Display, 5(1):61–67, Mar. 1997.

9. Fang, Y., “Joint source-channel estimation using accumulated LDPC syndrome,” IEEE

Commun. Lett., 14(11): 1044-1046, Nov. 2010.

10. Fang, Y., “EREC-based length coding of variable-length data blocks,” IEEE Trans. Circ.

Syst. Video Technol., 20(10): 1358-1366, Oct. 2010.

11. Fang, Y., “Distribution of distributed arithmetic codewords for equiprobable binary sources,”

IEEE Signal Process. Lett., 16(12), 1079-1082, Dec. 2009.

12. Fang, Y., “Crossover probability estimation using mean-intrinsic-LLR of LDPC syndrome,”

IEEE Commun. Lett., 13(9): 679-681, Sep. 2009.

13. Fang, Y., Jeon, G., and Jeong, J., “State-information-assisting EREC,” IEEE Signal Process.

Lett., 16(4): 260-263, Apr. 2009.

14. Fang, Y. and Jeong, J., “Correlation parameter estimation for LDPC-based Slepian-Wolf

coding,” IEEE Commun. Lett., 13(1): 37-39, Jan. 2009.

15. Jeon, G., Anisetti, M., Bellandi, V., and Jeong, J., “Fuzzy rule-based edge-restoration

algorithm in HDTV interlaced sequences,” IEEE Trans. Consumer Electronics, 53(2): 725-

731, May 2007.

16. Jeon, G., Anisetti, M., Bellandi, V., Damiani, E., and Jeong, J., “Rough Sets- Assisted

Subfield Optimization for Alternating Current Plasma Display Panel”, IEEE Trans.

Consumer Electronics, 53(3): 825-832, Aug. 2007.

17. Jeon, G., Anisetti, M., Bellandi, V., Damiani, E., and Jeong, J., “Fuzzy Weighted Approach

to Improve Visual Quality of Edge-Based Filtering” IEEE Transactions on Consumer

Electronics, November 2007.

18. Jeon, G., Anisetti, M., Kim, D., Bellandi, V., Damiani, E., and Jeong, J., “Fuzzy rough sets

hybrid scheme for motion and scene complexity adaptive deinterlacing”, IMAVIS Image and

Vision Computing Journal, 27(4): 425-436, March 2009.

19. Jeon, G., Anisetti, M., and Kang, S., “A Rank-Ordered Marginal Filter for Deinterlacing”

Sensors, 13(3): 3056-3065, March 2013.

Documents

Bit Plane Technique for Power Concerned Environment 1