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Title: Visual-quality guided global backlight dimming for video display on mobile devices
Authors: Yeh, Chia-Hung
Lo, Kyle Shih-Huang
Lin, Weisi
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Yeh, C.-H., Lo, K. S.-H., & Lin, W. (2019). Visual-quality guided global backlight dimming for video display on mobile devices. IEEE Transactions on Circuits and Systems for Video Technology, 29(11), 3393-3403. doi:10.1109/TCSVT.2018.2879094
Journal: IEEE Transactions on Circuits and Systems for Video Technology
Abstract: This proposes a visual-quality guided global backlight dimming (VQG-GBD) algorithm to reduce the power consumption of liquid-crystal display on mobile devices. We build a backlight scaling ratio (BSR) prediction model via visual-quality assessment that not only considers the display contents but also the backlight intensity while measuring video quality. Also, we add visual uncertainty as an indicator to dim the backlight without being noticed by observers. The VQG-GBD includes a training stage and an online stage. For the training stage, first, we collect videos with distinct attributes of brightness and uncertainty. Then, the subjective rating obtains the relationship among the visual quality, BSR, brightness, and visual uncertainty. Finally, we use the trust-region method to build the BSR prediction model. In the online stage, the model is applied to mobile devices for real-time video display and a BSR optimization strategy is proposed to eliminate the flicker effect between frames, followed by three techniques to accelerate the process: 1) motion vector extraction; 2) pixel subsampling to reduce the computation while analyzing frame content; and 3) GPU rendering to speed up the pixel compensation. The experimental results show that VQG-GBD achieves 21% of the power demand reduction on average for displaying videos on mobile devices while preserving good visual quality. The VQG-GBD delivers more power reduction than the state-of-the-art algorithm image integrity-based gray-level error control and multi-histogram-based gray-level error control by 10% and 8%, respectively.
ISSN: 1051-8215
DOI: 10.1109/TCSVT.2018.2879094
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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