Organic Light Emitting Diode (OLED) is rapidly emerging as the mainstream mobile display technology. This is posing new challenges on the design of energy-saving solutions for OLED displays, specifically intended for interactive devices such as smartphones, smartwatches and tablets. To this date, the standard solution is brightness scaling. However, the amount of the scaling is typically set statically (either by the user, through a setting knob, or by the system in response to predefined events such as low-battery status) and independently of the displayed image. In this work we describe a smart computing technique called Low-Overhead Adaptive Brightness Scaling (LABS), that overcomes these limitations. In LABS, the optimal content-dependent brightness scaling factor is determined automatically for each displayed image, on a frame-by-frame basis, with a low computational cost that allows real-time usage. The basic form of LABS achieves more than 35% power reduction on average, when applied to different image datasets, while maintaining the Mean Structural Similarity Index (MSSIM) between the original and transformed images above 97%.
Low-Overhead Adaptive Brightness Scaling for Energy Reduction in OLED Displays / Jahier Pagliari, Daniele; Di Cataldo, Santa; Patti, Edoardo; Macii, Alberto; Macii, Enrico; Poncino, Massimo. - In: IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING. - ISSN 2168-6750. - 9:3(2021), pp. 1625-1636. [10.1109/TETC.2019.2908257]
Low-Overhead Adaptive Brightness Scaling for Energy Reduction in OLED Displays
Jahier Pagliari, Daniele;Di Cataldo, Santa;Patti, Edoardo;Macii, Alberto;Macii, Enrico;Poncino, Massimo
2021
Abstract
Organic Light Emitting Diode (OLED) is rapidly emerging as the mainstream mobile display technology. This is posing new challenges on the design of energy-saving solutions for OLED displays, specifically intended for interactive devices such as smartphones, smartwatches and tablets. To this date, the standard solution is brightness scaling. However, the amount of the scaling is typically set statically (either by the user, through a setting knob, or by the system in response to predefined events such as low-battery status) and independently of the displayed image. In this work we describe a smart computing technique called Low-Overhead Adaptive Brightness Scaling (LABS), that overcomes these limitations. In LABS, the optimal content-dependent brightness scaling factor is determined automatically for each displayed image, on a frame-by-frame basis, with a low computational cost that allows real-time usage. The basic form of LABS achieves more than 35% power reduction on average, when applied to different image datasets, while maintaining the Mean Structural Similarity Index (MSSIM) between the original and transformed images above 97%.File | Dimensione | Formato | |
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Low-Overhead_Adaptive_Brightness_Scaling_for_Energy_Reduction_in_OLED_Displays.pdf
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https://hdl.handle.net/11583/2729880