GoCAD: GPU-Assisted Online Content-Adaptive Display Power Saving for Mobile Devices in Internet Streaming

Yao Liu, Mengbai Xiao, Ming Zhang, Xin Li, Mian Dong, Zhan Ma, Zhenhua Li, Songqing Chen
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引用次数: 15

Abstract

During Internet streaming, a significant portion of the battery power is always consumed by the display panel on mobile devices. To reduce the display power consumption, backlight scaling, a scheme that intelligently dims the backlight has been proposed. To maintain perceived video appearance in backlight scaling, a computationally intensive luminance compensation process is required. However, this step, if performed by the CPU as existing schemes suggest, could easily offset the power savings gained from backlight scaling. Furthermore, computing the optimal backlight scaling values requires per-frame luminance information, which is typically too energy intensive for mobile devices to compute. Thus, existing schemes require such information to be available in advance. And such an offline approach makes these schemes impractical. To address these challenges, in this paper, we design and implement GoCAD, a GPU-assisted Online Content-Adaptive Display power saving scheme for mobile devices in Internet streaming sessions. In GoCAD, we employ the mobile device's GPU rather than the CPU to reduce power consumption during the luminance compensation phase. Furthermore, we compute the optimal backlight scaling values for small batches of video frames in an online fashion using a dynamic programming algorithm. Lastly, we make novel use of the widely available video storyboard, a pre-computed set of thumbnails associated with a video, to intelligently decide whether or not to apply our backlight scaling scheme for a given video. For example, when the GPU power consumption would offset the savings from dimming the backlight, no backlight scaling is conducted. To evaluate the performance of GoCAD, we implement a prototype within an Android application and use a Monsoon power monitor to measure the real power consumption. Experiments are conducted on more than 460 randomly selected YouTube videos. Results show that GoCAD can effectively produce power savings without affecting rendered video quality.
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GoCAD: gpu辅助在线内容自适应显示在互联网流媒体中为移动设备省电
在互联网流媒体过程中,很大一部分电池电量总是被移动设备上的显示面板所消耗。为了降低显示器的功耗和背光缩放,提出了一种智能调暗背光的方案。为了在背光缩放中保持可感知的视频外观,需要计算密集的亮度补偿过程。然而,这一步,如果CPU执行现有方案建议,可以很容易地抵消从背光缩放获得的电力节省。此外,计算最佳的背光缩放值需要每帧亮度信息,这对于移动设备来说通常过于耗能。因此,现有的计划要求事先获得这些资料。而这种离线方式使得这些计划不切实际。为了解决这些挑战,在本文中,我们设计并实现了GoCAD,一个gpu辅助的在线内容自适应显示节能方案,用于互联网流媒体会话的移动设备。在GoCAD中,我们使用移动设备的GPU而不是CPU来减少亮度补偿阶段的功耗。此外,我们使用动态规划算法以在线方式计算小批量视频帧的最佳背光缩放值。最后,我们新颖地使用了广泛使用的视频故事板,这是一组预先计算的与视频相关的缩略图,可以智能地决定是否为给定的视频应用我们的背光缩放方案。例如,当GPU功耗将抵消调暗背光所节省的费用时,不进行背光缩放。为了评估GoCAD的性能,我们在Android应用程序中实现了一个原型,并使用Monsoon功率监视器来测量实际功耗。实验在460多个随机选择的YouTube视频上进行。结果表明,GoCAD可以在不影响渲染视频质量的情况下有效地节省功耗。
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