A novel dynamic false contour evaluation method for microdisplays

Dynamic False Contours (DFC) are a critical display quality issue in the digital driving of silicon-based OLED microdisplays, caused by the interaction between pixel gray-scale discretization and the human eye's integration effect. The perceptual characteristics of the human eye make DFC difficult to quantify using conventional instruments. Particularly in near-eye display applications, where high standards for image quality and visual comfort are required, DFC can significantly reduce the user experience. To address this challenge, this paper proposes a threshold-relative integration method that, by incorporating the human eye's integration effect, visual persistence characteristics, and motion scene image variation patterns, constructs a quantitative evaluation model consistent with the actual usage conditions of near-eye displays. The study first analyzes the luminance distribution patterns of images at different motion speeds and establishes a dynamic threshold integration model based on the human eye's temporal/spatial integration response characteristics. This method not only solves the problem of quantitatively evaluating DFC but also provides a reliable tool for optimizing hardware driving circuits and developing software image compensation algorithms. It is of great significance for enhancing the realism and visual comfort of images in near-eye display devices such as virtual reality and augmented reality. Moreover, the quantitative evaluation approach can be extended to other display quality research areas related to human visual perception.