Due to the significant power consumption of emissive display panels and the limited battery capacity of electronic devices, it is necessary to implement power-constrained display operations for video content. One of the effective power-constrained display techniques is power-constrained contrast enhancement (PCCE), which aims to reduce the power demands of the display while maintaining the quality of the content. However, PCCE has been mainly studied in the image domain. In this letter, we explore how PCCE can be applied to the video domain by revisiting the proportional-integral-derivative (PID) control. The proposed method consists of three key components: 1) The proportional term determines a power-saving ratio for each video frame based on its luminance, resulting in aggressive power-saving in bright video frames; 2) the integral term ensures that the target power-saving ratio is achieved over the entire video; 3) the differential term prevents abrupt temporal fluctuations in the power-saving ratio of consecutive video frames. The experimental results demonstrate that the proposed PID-based PCCE method achieves superior performance on two public video datasets. In particular, the proposed method achieves 5.8% improvements in VMAF compared to the previous method on the TVSum dataset.

Various power-constrained contrast enhance-ment (PCCE) techniques have been applied to an organic light emitting diode (OLED) display for reducing the pow-er demands of the display while preserving the image qual-ity. In this paper, we propose a new deep learning-based PCCE scheme that constrains the power consumption of the OLED displays while enhancing the contrast of the displayed image. In the proposed method, the power con-sumption is constrained by simply reducing the brightness a certain ratio, whereas the perceived visual quality is pre-served as much as possible by enhancing the contrast of the image using a convolutional neural network (CNN). Furthermore, our CNN can learn the PCCE technique without a reference image by unsupervised learning. Ex-perimental results show that the proposed method is supe-rior to conventional ones in terms of image quality assess-ment metrics such as a visual saliency-induced index (VSI) and a measure of enhancement (EME).1.