LAI et al.: CONTENT-BASED LCD BACKLIGHT POWER REDUCTION USING HISTOGRAM ANALYSIS 551Fig. 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 based Fig. 3. New backlight dimming algorithm.on 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: (5) (1) (2) (6) (3) (7)where is the luminance value from the RGB tocolor 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 thecording to (3) (Step 4) can be selected. In Step 5, if the abso- elimination of , (5) and (6) show that the proposed NIEA haslute difference between the mean value and the median value no distortion in the hue (H) or saturation (S)color space, so theis greater than 60 (decimal), it implies there is a large variation values of the new and are the same as the original Hin the image. Therefore, the NBDA will not change the LCD and S. However, (7) shows that the enhancing luminancebacklight 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 basedis divided into eight different levels, and the NBDA selects the on NBDA, NIEA compensates for the image contrast so thatproper 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 inB. 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 toenhancement approach does not cause distortion in hue (H) or approach the luminance enhancement curve consisting of 16saturation (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.
552 JOURNAL OF DISPLAY TECHNOLOGY, VOL. 7, NO. 10, OCTOBER 2011 visual quality, the structural similarity index metric (SSIM) was derived according to . 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. 10–13Fig. 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 settingConsequently, NIEA can improve the image quality. The for- is around 22 mA as measured by the current meter. Next, themula 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) andnoise ratio (PSNR) are adopted to evaluate image quality. How- 5 (hex), respectively. Because the difference between the twoever, as they are sometimes not well-matched to perceive the values is less than 60 (decimal), NBDA selects the current level
LAI et al.: CONTENT-BASED LCD BACKLIGHT POWER REDUCTION USING HISTOGRAM ANALYSIS 553Fig. 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,  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  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 . From the experimental results in Table I, the backlight currentFig. 9. Display platform. selected by NBDA, on average, reduces power consumption by 47%. This is superior to . 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 andperform 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
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 ENHANCEMENTFig. 12. Test Image 3.shows, the values are close to 1; thus, our proposed algorithmscan 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 contrastlower power consumption and image contrast enhancement: the level which compensates for the brightness of the image whenNBDA, and the new image enhancement algorithm (NIEA). The the user can identify no conspicuous changes in the image by the
LAI et al.: CONTENT-BASED LCD BACKLIGHT POWER REDUCTION USING HISTOGRAM ANALYSIS 555 TABLE III  E. Y. Oh, S. H. Baik, M. H. Sohn, K. D. Kim, H. J. Hong, J. Y. Bang, PSNR TO EVALUATE IMAGE QUALITY K. J. Kwon, M. H. Kim, H. Jang, J. K. Yoon, and I. J. Chung, “IPSmode dynamic LCD-TV realization with low black luminance and high con- trast by adaptive dynamic image control technology,” J. Soc. Inf. Dis- play, vol. 13, pp. 215–219, 2005.  C.-C. Sun, S.-J. Ruan, M.-C. Shie, and T.-W. Pa, “Dynamic contrast en- hancement based on histogram speciﬁcation,” IEEE Trans. Consumer Electron., vol. 51, no. 4, pp. 1300–1305, Nov. 2005.  H. Cho and O. Kwon, “A backlight dimming algorithm for low power and high image quality LCD applications,” IEEE Trans. Consumer Electron., vol. 55, no. 4, pp. 839–844, May 2009.  A. Bartolini, M. Ruggiero, and L. Benini, “Visual quality analysis for dynamic backlight scaling in LCD systems,” in Proc. IEEE Des. Autom. & Test in Eur. Conf. & Exhib., 2009, pp. 1428–1433.  M. Ruggiero, A. Bartolini, and L. Benini, “DBS4video: Dynamic lu- TABLE IV minance backlight scaling based on multi-histogram frame character- SSIM TO EVALUATE IMAGE QUALITY ization for video streaming application,” in Proc. 8th ACM EMSOFT, Atlanta, GA, 2008, pp. 109–118.  Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, Apr. 2004.  S. Lee, K. Um, and B. Choi, “A power reduction method for LCD backlight based on human visual characteristics,” in Proc. Int. Conf. on Consumer Electron., 2008, pp. 1–2. 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. degreeHVS quality. The experimental results show that the proposed from the National Taiwan University, in 1990 andNBDA algorithm, on average, reduces power consumption by 1997, respectively. From 1992 to 1993, he was with the Institute47%, 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. Into 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 REFERENCES Electrical Engineering, National Chung Hsing University, Taichung, Taiwan.  G. Z. Wang, F. C. Lin, and Y. P. 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