1. 550 JOURNAL OF DISPLAY TECHNOLOGY, VOL. 7, NO. 10, OCTOBER 2011
Content-Based LCD Backlight Power Reduction
With Image Contrast Enhancement Using
Histogram Analysis
Yeong-Kang Lai, Member, IEEE, Yu-Fan Lai, Student Member, IEEE, and Peng-Yu Chen
Abstract—In recent years, low-power technology has had a
significant impact on portable electronic devices; with mobile
devices, the low-power circuit design has become the primary
issue. At present, thin-film transistor liquid crystal display (TFT
LCD) is widely used in handheld mobile devices. In terms of the
overall system power consumption, TFT LCD power consumes
20%–45% of total system power due to different applications. The
backlight of an LCD display dominates the power consumption
of the whole system; controlling the backlight current to reduce
Fig. 1. Block diagram of the display system for backlight control.
the brightness and the contrast of LCDs can reduce the overall
power consumption. However, this may cause significant changes
in visual perception. In order to reduce the power consumption
and eliminate the visual changes, the issue becomes: how to reduce the image contrast ratio and sustain the quality of the image.
the current by adjusting brightness and contrast in accordance There has been some research on image contrast enhancement
with the current image. Based on content analysis, this paper pro- [9]–[12]. In [10], Raman and Hekstra proposed an architecture
poses two new algorithms: the new backlight-dimming algorithm and outlined an algorithm using histogram information of the
(NBDA) and the new image enhancement algorithm (NIEA). The image for backlight modulation in LCDs. In [12], Sun and Ruan
proposed methods can, on average, simultaneously reduce power
consumption by 47% and improve the image enhancement ratio proposed a dynamic histogram specification algorithm to im-
by 6.8%. Moreover, the structural-similarity index metric (SSIM) prove the image contrast. Although the above methods could
is used to evaluate image quality. effectively enhance the image contrast, they did not consider
Index Terms—Backlight determination, image enhancement, image quality for the entire system. In [13], Cho and Kwon pro-
liquid crystal display (LCD), power saving. 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-
I. INTRODUCTION uate image quality from the human visual system (HVS).
Fig. 1 shows the block diagram of our proposed display
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 being
system for backlight control designed to decrease the power
consumption of the LCD backlight and enhance the image
contrast to compensate for the image brightness. First, the
light-weight, and having a thin format, low radiation and high
image data are analyzed to determine whether the TFT LCD
image quality. However, LCDs also have certain disadvantages
backlight current increases or decreases. After the backlight
which need to be overcome, such as light leakage from the
dimming level is selected, the system enhances the image
liquid crystals and a non-ideal cross polarizer. Using a backlight
contrast based on the selected current level, and users almost
dimming algorithm can minimize these drawbacks [1]–[8]. Al-
notice no significant changes in image quality. Both of the
though LCDs have high dynamic range properties, the complex
proposed new algorithms, New Backlight Dimming Algorithm
algorithms and high-cost backlight structures are not suitable
(NBDA) and New Image Enhancement Algorithm (NIEA),
for small-size LCD products, such as mobiles, PDAs, digital
not only decrease power consumption, but also increase image
photo frames, and car GPSs. Moreover, due to a decrease in the
contrast to sustain image quality.
driving current of the backlight, the display backlight will turn
The paper is organized as follows: in Section II, the proposed
dark. Therefore, we propose the NIEA algorithm to enhance
algorithms, NBDA and NIEA, are introduced; in Section III,
the proposed algorithms are implemented on an FPGA platform,
Manuscript received January 15, 2011; revised May 22, 2011 and June 28, and the experimental data are measured. In addition, some com-
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 National
parison tables are shown in this section. Finally, a brief conclu-
Science Council, Republic of China, under Grant NSC 99-2221-E-005-113, and sion is given in Section IV.
in part by the Ministry of Education, Taiwan, under the ATU plan.
The authors are with the Department of Electrical Engineering, National II. PROPOSED ALGORITHMS
Chung Hsing University, Taichung 402, Taiwan (e-mail: yklai@dragon.nchu.
edu.tw). A. New Backlight Dimming Algorithm (NBDA)
Color versions of one or more of the figures are available online at http://
ieeexplore.ieee.org. As proposed, the NBDA selects the backlight current level.
Digital Object Identifier 10.1109/JDT.2011.2162314 This algorithm gets a display image from Host and then sends it
1551-319X/$26.00 © 2011 IEEE

2. 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, NBDA
calculates the mean value and median value of the displayed
image. A high mean value indicates that the backlight will be
controlled to select a low current to save the system power based
Fig. 3. New backlight dimming algorithm.
on different backlight current levels. Fig. 2 shows the NBDA
block diagram to control backlight by histogram analysis. The
image 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:
(5)
(1)
(2) (6)
(3)
(7)
where is the luminance value from the RGB to
color space, and is the probability density function. Ac-
Equation (4) shows that the generated output data,
cording to (1) and (2), the static values of the histogram can
, become times of the original input data,
be estimated. Otherwise, a different backlight current level ac-
(R, G, B), where is the contrast factor gain. Because of the
cording to (3) (Step 4) can be selected. In Step 5, if the abso-
elimination of , (5) and (6) show that the proposed NIEA has
lute difference between the mean value and the median value
no distortion in the hue (H) or saturation (S)color space, so the
is greater than 60 (decimal), it implies there is a large variation
values of the new and are the same as the original H
in the image. Therefore, the NBDA will not change the LCD
and S. However, (7) shows that the enhancing luminance
backlight current because of the image fidelity issue, and the
becomes times of the original luminance (V).
original settings are kept. For this study, the backlight current
After the LCD backlight current level is selected based
is divided into eight different levels, and the NBDA selects the
on NBDA, NIEA compensates for the image contrast so that
proper backlight current level in terms of the mean value of the
viewers notice no conspicuous changes in the image quality.
image histogram.
NIEA defines a luminance enhancement curve, as shown in
B. New Image Enhancement Algorithm (NIEA) Fig. 4, which splits the image pixels into 16 equal intervals.
An input, , can be mapped to an output, , by the
From the viewpoint of the color space, when the gray-level
luminance enhancement curve. Since the luminance enhance-
data of the image are input to the NIEA, the proposed image
ment curve is nonlinear, the piecewise linear method is used to
enhancement approach does not cause distortion in hue (H) or
approach the luminance enhancement curve consisting of 16
saturation (S); only the image luminance (V) is enhanced. The
line segments.
analysis is derived as follows:
The NIEA algorithm is shown in Fig. 5. In Step 1, the gray-
level data are input to NIEA pixel by pixel; then can be
(4) calculated for the corresponding image pixel. In Step 2, the new
are then calculated and output to the LCD panel.

3. 552 JOURNAL OF DISPLAY TECHNOLOGY, VOL. 7, NO. 10, OCTOBER 2011
visual quality, the structural similarity index metric (SSIM) was
derived according to [16]. HVS is highly adapted for extracting
structural information. The formulas are represented as follows:
(9)
(10)
(11)
where , , and are luminance, contrast
and structural similarity, respectively. Fig. 6 shows the block
diagram of the SSIM measurement system. In order to calculate
the value of SSIM, we rearrange (9) to (10) and (11), where
, and stand for mean, standard deviation and correlation
coefficient, respectively. The final value of SSIM is between 0
and 1. When the value is closer to 1, it signifies, from the HVS
perspective, that the extracted structural information of the two
images is almost the same.
Fig. 4. Piecewise-linear method of NIEA. III. IMPLEMENTATION AND PERFORMANCE ANALYSIS
The proposed algorithms are implemented on a field pro-
grammable gate array (FPGA) platform. The block diagram
of the FPGA platform is shown in Fig. 7. An external flash
memory (USB flash disk) and SDRAM module on board to
store 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 transformation
module, NBDA module, and NIEA module. The transforma-
tion module from the RGB to is implemented using
canonical-signed-digit (CSD) fixed-coefficient multiplier. The
NBDA and NIEA modules are implemented using hardware
description language (HDL), Verilog, according to the algo-
rithms of NBDA and NIEA (Figs. 3 and 5). Fig. 9 shows the
photo of the display platform, with a 3-in TFT LCD panel with
a resolution of 960 240 pixels to display the modified image.
The maximum voltage the platform could support is 9.6 V, and
the corresponding current is 25 mA. The circuits on FPGA read
the image data from flash memory and perform the proposed
NBDA and NIEA algorithms.
From the experimental results, the upper parts of Figs. 10–13
Fig. 5. New image enhancement algorithm (NIEA). show the original test images without the proposed algorithms
having been performed. Thus, the backlight controller does
not change the backlight current; the default current setting
Consequently, NIEA can improve the image quality. The for- is around 22 mA as measured by the current meter. Next, the
mula for is defined in (8): middle parts of Figs. 10–13 show the modified test images
using the proposed algorithms, NBDA and NIEA. The back-
light controller lowers the backlight current to reduce power
dissipation. Moreover, the lower parts of Figs. 10–13 show the
(8) histogram analysis used to determine the values of mean, me-
where –16, , dian, standard deviation and correlation coefficient to evaluate
represents the original image pixels (0–255) and the image quality.
represents the enhanced image pixels (0–255). Furthermore, NBDA and NIEA select the suitable backlight
current by using image histogram and enhancing the image con-
C. Image Quality Assessment Using SSIM Index trast to compensate for the image brightness; for example, the
Usually, the mean square error (MSE) and the peak signal-to- values of the mean and medium in Fig. 10 are C (hex) and
noise ratio (PSNR) are adopted to evaluate image quality. How- 5 (hex), respectively. Because the difference between the two
ever, as they are sometimes not well-matched to perceive the values is less than 60 (decimal), NBDA selects the current level

4. LAI et al.: CONTENT-BASED LCD BACKLIGHT POWER REDUCTION USING HISTOGRAM ANALYSIS 553
Fig. 6. Block diagram of the SSIM measurement system.
Fig. 7. Block diagram of the FPGA platform.
Fig. 8. Proposed architecture.
Fig. 10. Test Image 1.
In order to compare backlight power savings based on the
same comparison level, [5] proposed a backlight dimming al-
gorithm using a backlight dimming gray (BDG) level at 75% of
the histogram, which is defined as the characteristic of image
data. 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 current
Fig. 9. Display platform. selected by NBDA, on average, reduces power consumption by
47%. This is superior to [5]. However, NIEA not only increases
the image contrast but also sustains the image quality.
0 to drive the LCD panel. Then, the (R, G, B) data are input to In Tables II and III, the ratio of image enhancement and
perform NIEA, and NIEA adopts the piecewise linear method PSNR value are 6.8233% and 93.116 dB on average, respec-
to compensate for image brightness and obtain the final current tively. In order to obtain a good match with HVS quality, the
(9.2 mA). SSIM method is used to evaluate the images. As Table IV

5. 554 JOURNAL OF DISPLAY TECHNOLOGY, VOL. 7, NO. 10, OCTOBER 2011
Fig. 13. Test Image 4.
Fig. 11. Test Image 2.
TABLE I
COMPARISON OF BACKLIGHT POWER SAVING RATIO
TABLE II
RATIO OF IMAGE ENHANCEMENT
Fig. 12. Test Image 3.
shows, the values are close to 1; thus, our proposed algorithms
can sustain the original image quality.
NBDA adopts the content-based histogram analysis to select the
IV. CONCLUSION corresponding TFT LCD backlight current and decreases power
In this paper, we have proposed two algorithms to realize consumption. Moreover, the NIEA increases the image contrast
lower power consumption and image contrast enhancement: the level which compensates for the brightness of the image when
NBDA, and the new image enhancement algorithm (NIEA). The the user can identify no conspicuous changes in the image by the

6. LAI et al.: CONTENT-BASED LCD BACKLIGHT POWER REDUCTION USING HISTOGRAM ANALYSIS 555
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TABLE IV
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Yeong-Kang Lai (M’94) was born in Taipei, Taiwan,
in 1966. He received the B.S. degree in electrical
engineering from the Tamkang University, Taipei,
Taiwan, in 1988, and the M.S. and Ph.D. degree
HVS quality. The experimental results show that the proposed from the National Taiwan University, in 1990 and
NBDA algorithm, on average, reduces power consumption by 1997, respectively.
From 1992 to 1993, he was with the Institute
47%, while the proposed NIEA algorithm enhances the image of Information Science, Academia Sinica, Taiwan,
contrast ratio and sustains image quality. Finally, SSIM is used where he worked on video conference system. In
to measure image quality, which proves to be very close to that 1997, he joined the Electrical Engineering Depart-
ment, Chang Gung University, Taoyuan, Taiwan,
of the original image. as an Assistant Processor. From 1998 to 2001, he was Assistant Processor of
the Information Engineering Department at National Dong Hwa University,
Hualien, Taiwan. Currently, he is Associate Processor of the Department of
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