content based lcd backlight power reduction

550 JOURNAL OF DISPLAY TECHNOLOGY, VOL. 7, NO. 10, OCTOBER 2011

Content-Based LCD Backlight Power ReductionWith Image Contrast Enhancement Using

Histogram AnalysisYeong-Kang Lai, Member, IEEE, Yu-Fan Lai, Student Member, IEEE, and Peng-Yu Chen

Abstract—In recent years, low-power technology has had asignificant impact on portable electronic devices; with mobiledevices, the low-power circuit design has become the primaryissue. At present, thin-film transistor liquid crystal display (TFTLCD) is widely used in handheld mobile devices. In terms of theoverall system power consumption, TFT LCD power consumes20%–45% of total system power due to different applications. Thebacklight of an LCD display dominates the power consumptionof the whole system; controlling the backlight current to reducethe brightness and the contrast of LCDs can reduce the overallpower consumption. However, this may cause significant changesin visual perception. In order to reduce the power consumptionand eliminate the visual changes, the issue becomes: how to reducethe current by adjusting brightness and contrast in accordancewith the current image. Based on content analysis, this paper pro-poses two new algorithms: the new backlight-dimming algorithm(NBDA) and the new image enhancement algorithm (NIEA). Theproposed methods can, on average, simultaneously reduce powerconsumption by 47% and improve the image enhancement ratioby 6.8%. Moreover, the structural-similarity index metric (SSIM)is used to evaluate image quality.

Index Terms—Backlight determination, image enhancement,liquid crystal display (LCD), power saving.

I. INTRODUCTION

P OWER consumption and image quality both play impor-tant roles in liquid-crystal displays (LCDs). Compared to

cathode-ray tubes (CRTs), LCDs have the advantage of beinglight-weight, and having a thin format, low radiation and highimage quality. However, LCDs also have certain disadvantageswhich need to be overcome, such as light leakage from theliquid crystals and a non-ideal cross polarizer. Using a backlightdimming algorithm can minimize these drawbacks [1]–[8]. Al-though LCDs have high dynamic range properties, the complexalgorithms and high-cost backlight structures are not suitablefor small-size LCD products, such as mobiles, PDAs, digitalphoto frames, and car GPSs. Moreover, due to a decrease in thedriving current of the backlight, the display backlight will turndark. Therefore, we propose the NIEA algorithm to enhance

Manuscript received January 15, 2011; revised May 22, 2011 and June 28,2011; accepted July 04, 2011. Date of publication August 30, 2011; date of cur-rent version September 16, 2011. This work was supported in part by NationalScience Council, Republic of China, under Grant NSC 99-2221-E-005-113, andin part by the Ministry of Education, Taiwan, under the ATU plan.

The authors are with the Department of Electrical Engineering, NationalChung Hsing University, Taichung 402, Taiwan (e-mail: [email protected]).

Color versions of one or more of the figures are available online at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JDT.2011.2162314

Fig. 1. Block diagram of the display system for backlight control.

the image contrast ratio and sustain the quality of the image.There has been some research on image contrast enhancement[9]–[12]. In [10], Raman and Hekstra proposed an architectureand outlined an algorithm using histogram information of theimage for backlight modulation in LCDs. In [12], Sun and Ruanproposed a dynamic histogram specification algorithm to im-prove the image contrast. Although the above methods couldeffectively enhance the image contrast, they did not considerimage quality for the entire system. In [13], Cho and Kwon pro-posed a backlight dimming algorithm to reduce power consump-tion and improve image quality in LCD applications. In [14],[15], Bartolini and Ruggiero adopted the SSIM method to eval-uate image quality from the human visual system (HVS).

Fig. 1 shows the block diagram of our proposed displaysystem for backlight control designed to decrease the powerconsumption of the LCD backlight and enhance the imagecontrast to compensate for the image brightness. First, theimage data are analyzed to determine whether the TFT LCDbacklight current increases or decreases. After the backlightdimming level is selected, the system enhances the imagecontrast based on the selected current level, and users almostnotice no significant changes in image quality. Both of theproposed new algorithms, New Backlight Dimming Algorithm(NBDA) and New Image Enhancement Algorithm (NIEA),not only decrease power consumption, but also increase imagecontrast to sustain image quality.

The paper is organized as follows: in Section II, the proposedalgorithms, NBDA and NIEA, are introduced; in Section III,the proposed algorithms are implemented on an FPGA platform,and the experimental data are measured. In addition, some com-parison tables are shown in this section. Finally, a brief conclu-sion is given in Section IV.

II. PROPOSED ALGORITHMS

A. New Backlight Dimming Algorithm (NBDA)

As proposed, the NBDA selects the backlight current level.This algorithm gets a display image from Host and then sends it

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LAI et al.: CONTENT-BASED LCD BACKLIGHT POWER REDUCTION USING HISTOGRAM ANALYSIS 551

Fig. 2. Block diagram for NBDA.

to NBDA to analyze the image histogram. First, RGB is trans-formed to , and Y (luminance) is regarded as gray-level.By using a statistical analysis of the image histogram, NBDAcalculates the mean value and median value of the displayedimage. A high mean value indicates that the backlight will becontrolled to select a low current to save the system power basedon different backlight current levels. Fig. 2 shows the NBDAblock diagram to control backlight by histogram analysis. Theimage histogram represents the distribution of the gray level.The five steps of the NBDA algorithm are detailed in Fig. 3.The definitions of the mean and the median of the image his-togram are as follows:

(1)

(2)

(3)

where is the luminance value from the RGB tocolor space, and is the probability density function. Ac-cording to (1) and (2), the static values of the histogram canbe estimated. Otherwise, a different backlight current level ac-cording to (3) (Step 4) can be selected. In Step 5, if the abso-lute difference between the mean value and the median valueis greater than 60 (decimal), it implies there is a large variationin the image. Therefore, the NBDA will not change the LCDbacklight current because of the image fidelity issue, and theoriginal settings are kept. For this study, the backlight currentis divided into eight different levels, and the NBDA selects theproper backlight current level in terms of the mean value of theimage histogram.

B. New Image Enhancement Algorithm (NIEA)

From the viewpoint of the color space, when the gray-leveldata of the image are input to the NIEA, the proposed imageenhancement approach does not cause distortion in hue (H) orsaturation (S); only the image luminance (V) is enhanced. Theanalysis is derived as follows:

(4)

Fig. 3. New backlight dimming algorithm.

(5)

(6)

(7)

Equation (4) shows that the generated output data,, become times of the original input data,

(R, G, B), where is the contrast factor gain. Because of theelimination of , (5) and (6) show that the proposed NIEA hasno distortion in the hue (H) or saturation (S)color space, so thevalues of the new and are the same as the original Hand S. However, (7) shows that the enhancing luminancebecomes times of the original luminance (V).

After the LCD backlight current level is selected basedon NBDA, NIEA compensates for the image contrast so thatviewers notice no conspicuous changes in the image quality.NIEA defines a luminance enhancement curve, as shown inFig. 4, which splits the image pixels into 16 equal intervals.An input, , can be mapped to an output, , by theluminance enhancement curve. Since the luminance enhance-ment curve is nonlinear, the piecewise linear method is used toapproach the luminance enhancement curve consisting of 16line segments.

The NIEA algorithm is shown in Fig. 5. In Step 1, the gray-level data are input to NIEA pixel by pixel; then can becalculated for the corresponding image pixel. In Step 2, the new

are then calculated and output to the LCD panel.


Fig. 4. Piecewise-linear method of NIEA.

Fig. 5. New image enhancement algorithm (NIEA).

Consequently, NIEA can improve the image quality. The for-mula for is defined in (8):

(8)where –16, ,

represents the original image pixels (0–255) andrepresents the enhanced image pixels (0–255).

C. Image Quality Assessment Using SSIM Index

Usually, the mean square error (MSE) and the peak signal-to-noise ratio (PSNR) are adopted to evaluate image quality. How-ever, as they are sometimes not well-matched to perceive the

visual quality, the structural similarity index metric (SSIM) wasderived according to [16]. HVS is highly adapted for extractingstructural information. The formulas are represented as follows:

(9)

(10)

(11)

where , , and are luminance, contrastand structural similarity, respectively. Fig. 6 shows the blockdiagram of the SSIM measurement system. In order to calculatethe value of SSIM, we rearrange (9) to (10) and (11), where

, and stand for mean, standard deviation and correlationcoefficient, respectively. The final value of SSIM is between 0and 1. When the value is closer to 1, it signifies, from the HVSperspective, that the extracted structural information of the twoimages is almost the same.

III. IMPLEMENTATION AND PERFORMANCE ANALYSIS

The proposed algorithms are implemented on a field pro-grammable gate array (FPGA) platform. The block diagramof the FPGA platform is shown in Fig. 7. An external flashmemory (USB flash disk) and SDRAM module on board tostore the original images data and the modified image data, re-spectively, are required. Fig. 8 shows our proposed architecture.The architecture consists of three parts: color transformationmodule, NBDA module, and NIEA module. The transforma-tion module from the RGB to is implemented usingcanonical-signed-digit (CSD) fixed-coefficient multiplier. TheNBDA and NIEA modules are implemented using hardwaredescription language (HDL), Verilog, according to the algo-rithms of NBDA and NIEA (Figs. 3 and 5). Fig. 9 shows thephoto of the display platform, with a 3-in TFT LCD panel witha resolution of 960 240 pixels to display the modified image.The maximum voltage the platform could support is 9.6 V, andthe corresponding current is 25 mA. The circuits on FPGA readthe image data from flash memory and perform the proposedNBDA and NIEA algorithms.

From the experimental results, the upper parts of Figs. 10–13show the original test images without the proposed algorithmshaving been performed. Thus, the backlight controller doesnot change the backlight current; the default current settingis around 22 mA as measured by the current meter. Next, themiddle parts of Figs. 10–13 show the modified test imagesusing the proposed algorithms, NBDA and NIEA. The back-light controller lowers the backlight current to reduce powerdissipation. Moreover, the lower parts of Figs. 10–13 show thehistogram analysis used to determine the values of mean, me-dian, standard deviation and correlation coefficient to evaluatethe image quality.

Furthermore, NBDA and NIEA select the suitable backlightcurrent by using image histogram and enhancing the image con-trast to compensate for the image brightness; for example, thevalues of the mean and medium in Fig. 10 are C (hex) and5 (hex), respectively. Because the difference between the twovalues is less than 60 (decimal), NBDA selects the current level


Fig. 6. Block diagram of the SSIM measurement system.

Fig. 7. Block diagram of the FPGA platform.

Fig. 8. Proposed architecture.

Fig. 9. Display platform.

0 to drive the LCD panel. Then, the (R, G, B) data are input toperform NIEA, and NIEA adopts the piecewise linear methodto compensate for image brightness and obtain the final current(9.2 mA).

Fig. 10. Test Image 1.

In order to compare backlight power savings based on thesame comparison level, [5] proposed a backlight dimming al-gorithm using a backlight dimming gray (BDG) level at 75% ofthe histogram, which is defined as the characteristic of imagedata. This backlight-dimming ratio is calculated as

. For example, the BDR of Fig. 10 is equal to. The backlight current of [5] mA

mA, the power saving of the backlight. However, the power saving of

our NBDA is . Hence the pro-posed algorithm saves more power than [5].

From the experimental results in Table I, the backlight currentselected by NBDA, on average, reduces power consumption by47%. This is superior to [5]. However, NIEA not only increasesthe image contrast but also sustains the image quality.

In Tables II and III, the ratio of image enhancement andPSNR value are 6.8233% and 93.116 dB on average, respec-tively. In order to obtain a good match with HVS quality, theSSIM method is used to evaluate the images. As Table IV




shows, the values are close to 1; thus, our proposed algorithmscan sustain the original image quality.

IV. CONCLUSION

In this paper, we have proposed two algorithms to realizelower power consumption and image contrast enhancement: theNBDA, and the new image enhancement algorithm (NIEA). The


TABLE ICOMPARISON OF BACKLIGHT POWER SAVING RATIO

TABLE IIRATIO OF IMAGE ENHANCEMENT

NBDA adopts the content-based histogram analysis to select thecorresponding TFT LCD backlight current and decreases powerconsumption. Moreover, the NIEA increases the image contrastlevel which compensates for the brightness of the image whenthe user can identify no conspicuous changes in the image by the


TABLE IIIPSNR TO EVALUATE IMAGE QUALITY

TABLE IVSSIM TO EVALUATE IMAGE QUALITY

HVS quality. The experimental results show that the proposedNBDA algorithm, on average, reduces power consumption by47%, while the proposed NIEA algorithm enhances the imagecontrast ratio and sustains image quality. Finally, SSIM is usedto measure image quality, which proves to be very close to thatof the original image.

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Yeong-Kang Lai (M’94) was born in Taipei, Taiwan,in 1966. He received the B.S. degree in electricalengineering from the Tamkang University, Taipei,Taiwan, in 1988, and the M.S. and Ph.D. degreefrom the National Taiwan University, in 1990 and1997, respectively.

From 1992 to 1993, he was with the Instituteof Information Science, Academia Sinica, Taiwan,where he worked on video conference system. In1997, he joined the Electrical Engineering Depart-ment, Chang Gung University, Taoyuan, Taiwan,

as an Assistant Processor. From 1998 to 2001, he was Assistant Processor ofthe Information Engineering Department at National Dong Hwa University,Hualien, Taiwan. Currently, he is Associate Processor of the Department ofElectrical Engineering, National Chung Hsing University, Taichung, Taiwan.He is also a member of the honor society Phi Tau Phi. His research interestsinclude video compression, DSP architecture design, video signal processordesign, and VLSI signal processing.

Yu-Fan Lai (S’06) was born in Taichung, Taiwan, onJune 14, 1978. He received the B.S. degree in auto-matic control engineering from the Feng Chia Uni-versity, Taichung, Taiwan, in 2000, and the M.S. de-gree in electrical engineering from the Chung HwaUniversity, Hsinchu, Taiwan, in 2003. From 2003 to2007, he ever worked in Ritek Corporation, Hsinchu,Taiwan. He is currently pursuing the Ph.D. degree inthe department of electrical engineering at NationalChung Hsing University.

His major research interests include image andvideo processing, VLSI architecture design of image and video coding, andVLSI design for digital signal processing.

Peng-Yu Chen received the M.S. degree in electricalengineering from the National Chung Hsing Univer-sity, Taichung, Taiwan.

His major research interests include image andvideo processing, FPGA architecture design ofimage and video coding, and FPGA design fordigital signal processing.