Power constrained exposure correction network for mobile devices

Yi-Yu Chou, Muhamad Amirul Haq, Shanq-Jang Ruan, Peter Chondro

Abstract: The Active-Matrix Organic Light-Emitting Diodes (AMOLED) technology has become the mainstream of displays in recent years. However, it will generate a lot of power consumption on AMOLED displays when displaying high-brightness content. To address this problem, an exposure correction mechanism is needed to remove high-brightness ambient light in the image. This work proposes a Power-Constrained Exposure Correction (PCEC) network based on adversarial learning. The PCEC network utilizes a U-Net-based generator with self-regularized high-exposure attention and adopts the global-local discriminator architecture for adversarial learning. To reduce the intensity of the high-exposed regions while constraining the AMOLED display’s power consumption, we include a power-constraint algorithm in the generator. The experimental results on three different datasets show that the proposed method can effectively correct high-exposed regions and achieve an average of 22.69% power-saving rate in the high-quality mode and 68.71% in the high-efficiency mode. The proposed method achieves 82.4 milliseconds of average inference time when run on a mobile device. Furthermore, the proposed method can enhance the saturation and contrast of the image and provide better visual quality than the existing power-constrained over-exposure correction methods.