Backlight dimming based on saliency map acquired by visual attention analysis |
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| Affiliation: | 1. Department of Electronics and Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea;2. Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea;1. School of Electronic Engineering, Xidian University, Xi’an 710071, China;2. College of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, Republic of Korea;1. Colorado State University, Fort Collins, CO 80523, USA;2. U.S. Department of the Air Force, Roy, UT 84067, UT 84067, USA;3. Woodward, Inc., Fort Collins, CO 80525, USA;1. State Key Lab of CAD&CG, Zhejiang University, Zijingang Campus, 310058 Hangzhou, Zhejiang, China;2. Department of Computer Science, Virginia Tech, United States;3. College of Informatics, Zhejiang University of Finance & Economics, China;4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hongkong;1. Dept. of Electrical and Electronic Engineering, Yonsei Univ., 134 Shinchon-Dong, Seodaemun-Gu, Seoul, South Korea;2. Dept. of Computational Science & Engineering, Yonsei Univ., 134 Shinchon-Dong, Seodaemun-Gu, Seoul, South Korea |
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| Abstract: | Displays have been used in various applications from mobile phones to tablets, and the low power consumption is one of their most important factors. Backlight dimming is the most promising technique to achieve this because it significantly reduces the display power by controlling only the transmittance of liquid crystal. This paper proposes a new backlight dimming algorithm using visual attention analysis. Conventional algorithms have a serious saturation error in some images when performing backlight dimming, thereby degrading image quality. In contrast, the proposed algorithm analyzes image characteristics based on the saliency map, which considers human visual attention. It then truncates the meaningless information of the saliency map using an adaptive saliency level selection approach and calculates the maximum amount of saturation error that humans will not perceive. In addition, the proposed algorithm defines the objective function and computes the optimal starting gray level in that function to calculate the saturation error. Simulation results show that the proposed algorithm using the adaptive saliency level selection approach performs best. In addition, the average peak signal-to-noise ratio of the proposed algorithm was up to 3.762 dB higher than that of the conventional algorithm while slightly increasing the power consumption. |
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| Keywords: | Backlight dimming Saliency map Liquid crystal display |
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