In addressing the high power consumption of near-eye display devices, this paper introduces a region gradient dimming algorithm that adapts to the human eye's gaze point, informed by studies on human visual characteristics. Initially, a central fovea-brightness masking experiment established the Just Noticeable Difference (JND) threshold for the human eye. This data refined the traditional geometric optical path model of the eye gaze point, resulting in a model that aligns more closely with the eye's adaptive characteristics. The method significantly improved the efficiency of viewing angle calculations through a maximum viewing angle discrimination approach. An enhanced contrast algorithm was then applied during image preprocessing to improve the display quality while preserving the image's average brightness. Utilizing the JND threshold and gaze point data, the algorithm applied both regional and global power consumption limits to the image, reducing power usage while maintaining subjective visual perception. The algorithm underwent validation on an FPGA hardware platform, demonstrating a reduction in display power consumption for silicon-based Organic Light Emitting Diode (OLED) microdisplays by up to 23.05% on the Kodak standard test set. This achievement suggests that the low-power requirements for silicon-based OLED microdisplays are achievable, offering insights for performance enhancements.