Power-preserving degradation for energy-aware images

Deep learning has been the state-of-the-art for transforming an image into its energy-aware version to lower the display power consumption of organic light-emitting diode (OLED). Most of the existing works achieve high-quality metrics resorting to high computation cost. Moreover, under an intense power-saving rate, the quality degradation of the power-aware image becomes more severe. In this work, we propose a novel training strategy for a deep neural network that explicitly address the degradation that occurs in power-aware image in which the intensity distribution is shrunk and shifted to a lower intensity. Our training strategy, referred to as power-preserving degradation (PPD), deliberately obstructs the distribution of the intensity and the details of an input image in the training phase. Hence, the network learns to optimize the utilization of dynamic range and quality metrics. In addition, the power-preserving term mitigates the power error due to the change in the distribution. We pair our training strategy with a simple and lightweight network that processes the input in two stages. The input is decomposed into a feature set, and a power-attention mechanism is applied to reduce intensity with on-demand power. We experiment with high power-saving rates of 40%, 50%, 60%, and 70% on three image datasets. As a result, our proposed strategy predominantly obtains the highest scores metrics. For instance, under 70% power-saving, it achieves a 0. 85 structural similarity score.