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Wei-Chung Cheng, Member, IEEE, Massoud Pedram, Fellow, IEEE - page 7 / 8

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CBCS(p[0..255],k) cdf[0]=p[0]; for (i=0; i<256; i++) cdf[i]+=p[i]; {

for (b=b Pb=P min backlight ; b<=bmax (b);

; b+=(1/k))

{

for (dr=1; dr<=255; dr+=(256/k)) { Rmax=-1; for (g=0; g<=255-dr; g+=(256/k)) R=cdf[g+dr]-cdf[g];

{

if (R>Rmax gl=g;

)

{

Rmax

=R;

}

}

} if (b>=dr)

Fc=R; else

Fc=(b/dr)*R; gu=gl+dr; Sol = <Fc,Pb,b,gl,gu>; Search solution database for

<Fc,*,*,*> and <*,Pb,*,*>; if (Sol is not inferior)

Insert Sol into solution database;

}

}

Figure 7: CBCS Optimization Flow.

6e, in comparison with Fig. 6f generated from the brightness-invariant policy from (3). The procedures of CBCS are summarized in Figure 7.

V. EXPERIMENTAL RESULTS

We use a set of benchmark images from the USC SIPI Image Database (USID) 0. The USID is considered the de facto benchmark suite in the signal and image processing research field [5]. The results reported here are from 8 color images from volume 3. All of them have 256 by 256 pixels. The color depth is 24 bits, i.e., 8 bits per color- channel in the range of 0-255.

Table I and II show the optimal CBCS policies for the benchmark images. We use 90% as the global contrast fidelity threshold to find the minimum backlight factor and its optimal linear transformation. The results show an average of 3.7X power savings within 10% of contrast distortion.

4.1.01

0.51

1

0.00

0.91

803.84

4.1.02

0.38

1

0.00

0.91

549.99

4.1.03

0.65

1

0.00

0.91

1077.21

4.1.04

0.75

1

0.00

0.91

1272.47

4.1.05

0.75

1

0.01

0.91

1272.47

4.1.06

0.84

1

0.04

0.90

1448.21

4.1.07

0.71

1

0.06

0.90

1194.36

4.1.08

0.72

1

0.04

0.92

1213.89

Backlight

Contrast

Brightness

Overall

CCFL

factor

fidelity

shift

fidelity

Power

b

c

d

Fc

(mW)

Optimal CBCS

Table I solutions to the USID benchmark images

Imag

#

e

Table II Original images (upper) vs. CBCS images (lower)

VI. CONCLUSIONS AND FUTURE WORK

We have presented the CBCS technique for a CCFL backlit TFT-LCD display. The proposed technique aims at conserving power by reducing the backlight illumination while retaining the image fidelity through preservation of the image contrast. We have explained how CCFL works and shown how to model the non-linearity between its backlight illumination and power consumption. We have proposed the contrast distortion metric to quantify the image quality loss after backlight scaling. We have formulated and optimally solved the CBCS optimization problem subject to contrast distortion. Experimental results show that an average of 3.7X power savings can be achieved with 10% of contrast distortion.

The CBCS technique we propose in this paper is only for still images. Future studies, however, should consider applying it to video applications. Since the decision of the backlight factor is based on each frame individually, the backlight factor may change significantly across consecutive frames because the histograms vary significantly. The huge change in the backlight factor will introduce inter-frame brightness distortion to the observer. When the CBCS technique is to be applied to video applications such as an MPEG2 decoder, the change of the backlight factor should be limited such that the change is subtle enough to be insensible by human eyes.

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