Adaptive Parallax-Based Contrast Enhancement for Power-Efficient Stereoscopic OLED Displays

Organic light-emitting diode (OLED)-based augmented and virtual reality (AR/VR) technologies are becoming increasingly integral to everyday life. However, these devices often face significant challenges, including high energy consumption and insufficient charging capabilities under typical usage conditions. Moreover, ensuring stereo consistency remains a significant issue in AR/VR devices. While stereo consistency has been extensively studied, most existing research has focused on stereo image restoration, with limited attention to power-constrained contrast enhancement (PCCE) in practical applications. To address these challenges, we propose an adaptive parallax-based contrast enhancement method for stereoscopic OLED displays aimed at reducing power consumption while enhancing visual contrast in the context of stereo consistency challenges. Specifically, our approach introduces a residual parallax attention module (RPAM), which dynamically adapts to different levels of parallax complexity and efficiently extracts multi-scale features, enhancing the overall consistency and quality of the stereo image pairs. Meanwhile, dilated convolution and channel attention mechanisms are incorporated within a power-aware attention module. Local enhancement techniques are integrated to adaptively regulate feature brightness in stereo pairs, enabling dynamic self-view and cross-view feature brightness adjustment. We compared our method with previous deep learning-based single-view PCCE methods and also conducted ablation studies on the parallax attention module (PAM). Image quality assessment metrics were used to evaluate image similarity and contrast. Comprehensive experiments conducted on three benchmark datasets confirm the effectiveness of our proposed approach in stereo PCCE methods.