A backlight optimization scheme for video playback on mobile devices

Abstract

For a typical portable handheld device, the backlight accounts for a significant percentage of the total energy consumption (e.g., around 30% for a Compaq iPAQ 3650). Substantial energy savings can be achieved by dynamically adapting backlight intensity levels on such low- power portable devices. In this paper, we analyze the characteristics of video streaming services and propose an adaptive scheme called Quality Adapted Backlight Scaling (QABS), to achieve backlight energy savings for video playback applications on handheld devices. Specifically, we present a fast algorithm to optimize backlight dimming while keeping the degradation in image quality to a minimum so that the overall service quality is close to a specified threshold. Additionally, we propose two effective techniques to prevent frequent backlight switching, which negatively affects user perception of video. Our initial experimental results indicate that the energy used for backlight is significantly reduced, while the desired quality is satisfied. The proposed algorithms can be realized in real time. I. INTRODUCTION With the widespread availability of 3G cellular networks, mobile hand-held devices are increasingly being designed to support stream- ing video content. These devices have stringent power constraints because they use batteries with finite lifetime. On the other hand, multimedia services are known to be very resource intensive and tend to exhaust battery resources quickly. Therefore, conserving power to prolong battery life is an important research problem that needs to be addressed, specifically for video streaming applications on mobile handheld devices. Most hand-held devices are equipped with a TFT (Thin-Film Transistor) LCD (Liquid Crystal Display). For these devices, the display unit is driven by the illumination of backlight. The backlight consumes a considerable percentage of the total energy usage of the handheld device; it consumes 20%-40% of the total system power (for Compaq iPAQ) (?). Dynamically dimming the backlight is considered an effective method to save energy (?), (?), (?) with scaling up of the pixel luminance to compensate for the reduced fidelity. The luminance scaling, however, tends to saturate the bright part of the picture, thereby affecting the fidelity of the video quality. In (?), a dynamic backlight luminance scaling (DLS) scheme is proposed. Based on different scenarios, three compensation strategies are discussed, i.e., brightness compensation, image enhancement, and context processing. However, their calculation of the distortion does not consider the fact that the clipped pixel values do not contribute equally to the quality distortion. In (?), a similar method, named concurrent brightness and contrast scaling (CBCS), is proposed. CBCS aims at conserving power by reducing the backlight illumi- nation while retaining the image fidelity through preservation of the image contrast. Their distortion definition and proposed compensation technique may be good for static image based applications, such as the graphic user interface (GUI) and maps, but might not be suitable for streaming video scenarios, because their contrast compensation further compromises the fidelity of the images. In addition, neither (?) nor (?) solves the problem associated with frequent backlight switching which can be quite distracting to the end user. In this paper, we explicitly incorporate video quality into the backlight switching strategy and propose a quality adaptive back- light scaling (QABS) scheme. The backlight dimming affects the brightness of the video. Therefore, we only consider the luminance compensation such that the lost brightness can be restored. The lumi- nance compensation, however, inevitably results in quality distortion. For the video streaming application, the quality is normally defined as the resemblance between the original and processed video. Hence, for the sake of simplicity and without loss of generality, we define the quality distortion function as the mean square error (MSE)(see Equation (1)) and the quality function as the peak signal to noise ratio (PSNR)(see Equation (2)), both of which are well accepted objective video quality measurements.